Journal of Image and Graphics aims to publish original, previously unpublished, research, reviews, communication, plus case studies and short research notes, on both applied and theoretical aspects in imaging and graphics research.
Contrastive Vision Transformer Combined with Hyperparameter Fine-Tuning and Interpretable AI for Glaucoma Assessment
Glaucoma, often known as the ‘silent thief of sight’, is a leading cause of irreversible blindness, which affects around 80 million people worldwide and hence its early and reliable detection is critical for preventing permanent vision loss. Although Vision Transformer (ViT)-based deep learning models are employed by existing techniques for automated glaucoma screening, yet, they primarily rely on conventional supervised training and single-dataset evaluation, which result in limited feature discrimination, suboptimal generalization across heterogeneous images, and poor clinical interpretability. To address these limitations, this study proposes a novel contrastive learning-optimized ViT framework, which integrates supervised contrastive pre-training with systematic hyperparameter optimization to learn more discriminative retinal features, and fine-tuning for glaucoma classification. In addition, a unified preprocessing and patch-based representation strategy is introduced to mitigate domain shifts across multiple imaging devices and acquisition protocols. Unlike prior studies using single benchmarks, this framework is validated on comprehensive multi-dataset setting combining six public fundus datasets (including G1020, ORIGA, REFUGE, PAPILA) to assess real-world generalization. Experimental results demonstrate consistent and statistically significant improvements over Convolutional Neural Network (CNN), baseline ViT models, in terms of achieving up to 87.91% accuracy and performance gains of 3-16% across accuracy, precision, recall, and F1score metrics. Further, Layer-wise Relevance Propagation (LRP) is employed to generate clinically interpretable heatmaps, which confirms that the model focuses on anatomically meaningful regions such as the optic disc and optic nerve head. These findings prove that the proposed framework provides robust, explainable, and generalizable solution for automated glaucoma screening and highlights its potential for clinical deployment.
Topic: Machine Learning Based Techniques for Image and Video Processing
Copyright (c) 2026 Journal of Image and Graphics
Robust Hybrid Deep Learning and Filtering Model for Occlusion Underwater Object Tracking
Underwater object detection plays a vital role in marine exploration, surveillance, and ecological monitoring. However, the presence of light scattering, color distortion, and low visibility makes accurate detection and tracking in underwater environments extremely challenging. To address these issues, this work proposes an efficient underwater object detection and tracking framework integrating the Single Shot MultiBox Detector (SSD), Deep Simple Online and Realtime Tracking (SORT), and Kalman Filter (KF). The SSD model is employed for real-time object detection due to its capability of multi-scale feature extraction and fast inference. Detected objects are then tracked across frames using the Deep SORT algorithm, which combines appearance descriptors and motion information for robust identity preservation. To further enhance trajectory estimation and reduce noise, the Kalman Filter is incorporated to predict object positions in occluded or visually degraded frames. The proposed hybrid approach demonstrates improved stability and accuracy in underwater video sequences utilizing the benchmark Underwater Object Tracking (UOT32) dataset. Performance evaluation based on precision, recall, and F1-Score confirms the model’s robustness in identifying and tracking objects under varying underwater conditions. The obtained results with a precision of 97.39%, a recall 100%, and F1-Score of 0.986 indicate that the integration of SSD with Deep SORT and Kalman filtering offers a reliable and computationally efficient solution for real-time underwater detection and tracking applications.
Enhancing Autism Classification Accuracy on Facial Images Using a CNN Model Decision Fusion
Autism Spectrum Disorder (ASD) is a neurodevelopmental condition affecting social interaction, communication, and behavior. Early detection is critical, yet conventional diagnostic methods are often time-consuming and resource-intensive. This study proposes a deep learning framework based on decision fusion of multiple Convolutional Neural Network (CNN) architectures to classify facial images of children with and without ASD. Six architectures (ResNet, DenseNet, Xception, ShuffleNet, MobileNetV2, and EfficientNet) were fine-tuned through transfer learning, and their predictions were integrated via decision fusion to improve generalization and classification accuracy. Experiments were conducted on a primary dataset of 1,050 facial images collected from 70 children (35 ASD, 35 typically developing) aged 5 to 15 years, recruited from special and regular elementary schools in Banda Aceh, Indonesia. Data augmentation was applied to address the limited sample size. Model performance was evaluated using accuracy, sensitivity, specificity, precision, and F1-Score. Among the combinations tested, ResNet and EfficientNet together yielded the best results, achieving 90% accuracy and a 91% F1-Score. These findings suggest that decision fusion is a practical and scalable approach for early ASD screening in resource-limited settings.
A Controlled Comparison of BiLSTM and Transformer Encoders for Arabic Handwritten Word Recognition
Despite significant advances in Arabic Handwritten Word Recognition (AHWR), the specific contribution of sequential encoders remains unclear because most prior studies change several architectural elements simultaneously, making comparisons difficult to interpret. This work presents the first carefully controlled evaluation of Bidirectional Long Short-Term Memory (BiLSTM) and Transformer encoders within an identical Convolutional Neural Network-Connectionist Temporal Classification (CNN-CTC) framework. All external factors, including preprocessing, the truncated ResNet-50 backbone, CTC alignment, Word Beam Search (WBS) decoding, and dataset splits, are held constant to isolate the effect of the sequence modeling mechanism itself, a dimension not explicitly analyzed in previous literature. The study also evaluates three augmentation strategies, including uniform augmentation and frequency-aware schemes at both the word and character levels, which address the distributional imbalance inherent in Arabic handwriting datasets. Experiments on IFN/ENIT show that the Transformer consistently achieves higher accuracy, up to 98.91%-Character Accuracy (CAR) and 98.41%-Word Accuracy (WAR), while the BiLSTM offers substantially faster inference. These findings provide the first reproducible quantification of the accuracy-efficiency trade-off between recurrent and attention-based encoders for Arabic cursive handwriting under fully controlled conditions.
Arabic Handwritten Word Recognition, ResNet-50, Transformer Encoder, Attention Mechanism, Connexionist Temporel Classification, Word Beam Search, Data Augmentation
Brain Fog Consortium Image Analysis Through Internet of Things Using Fog Computing in Stratus Cloud
The functioning of human organs is regulated by the central hub of the brain. Brain fog is characterized by a sensation of mental cloudiness. It affects the quality of life, memory impairment, and Alzheimer’s disease. Recent investigations have been conducted for the analysis of the brain fog consortium. Brain fog is defined as confusion, diminished mental clarity, forgetfulness, and impaired concentration. Also, it signifies a shortage in nutrients, a sleep disorder, depression, or a thyroid disease. This research proposes the identification of a brain fog consortium utilizing the Internet of Thing (IoT) and fog computing, alongside the suggested stratus cloud system for the brain through IoT integration. It evaluates cognitive impairments, categorizes genuine diseases, and performs analyses using a fog computing platform. The proposed study focuses on investigating segmentation diseases and their excision from the brain’s orientational organum, along with prospective detection methodologies within the anatomical structure. The reduction of brain fog can be achieved through the rapid advancement of IoT cloud computing and fog computing services. Experimental results, derived from the data generation of the proposed method, demonstrate efficiency levels in terms of density, affinity ratios, energy consumption, and storage relative to compute time for detecting brain fog. The accuracy of the propagated results is notable, and the implementation within the fog computing gateway is of high qualitative standard.
Feature Selection using Adaptive Mutation Probability based Starfish Optimization Algorithm in Diabetic Retinopathy Classification
Diabetic retinopathy (DR) is a common ocular complication of diabetes and a leading cause of vision impairment. Grading the severity of DR plays a crucial role in early diagnosis and timely intervention. However, manual diagnosis is often time-consuming, resource-intensive, and susceptible to human error. In contrast, computer-aided diagnosis (CAD) systems provide faster, more consistent, and potentially more accurate alternative, while enabling more effective screening and treatment planning. Thus, this research proposes the feature selection approach of Adaptive Mutation Probability based Starfish Optimization Algorithm (AMP-SFOA) for the automatic diagnosis and classification of DR into multiple classes. Primarily, this research considers two standard dataset, ATPOS-2019 and DDR to implement the proposed method based on training and testing. Then, Image resizing, Contrast Limited Adaptive Histogram Equalization (CLAHE) and Data Augmentation are performed to improve the input data. Finally, the Long Short-Term Memory (LSTM) is used for the classification of DR into multi-classes. The experimental results demonstrates that the proposed AMP-SFOA approach reaches better accuracy of 99.476% and 96.958% on APTOS-2019 dataset and DDR dataset respectively as compared to the existing methods such as RT2Net and Swin-TransformerV2 Multi-Branch Fine-grained Diabetic Retinopathy Grade Classification (STMF-DRNet).
contrast limited adaptive histogram equalization
Gaussian Splatting for Photorealistic Immersive Virtual Tours
To achieve photorealistic Virtual Reality (VR) tours, a balance is required between visual fidelity, responsiveness, and the strict runtime and memory constraints of head-mounted displays. As a more recent explicit view synthesis method, three-Dimensional Gaussian Splatting (3DGS) has been proposed as a real-time alternative to Neural Radiance Fields (NeRF). In this study, we present an overview of 3DGS, demonstrating its application to immersive VR tours, and propose a design philosophy for its implementation on standalone headsets. Specifically, we review state-of-the-art 3DGS variants with respect to photorealism, scalability, efficiency, dynamics, and semantics; analyze VR-specific constraints related to latency, stereo stability, and thermal, memory, and interaction requirements; collate VR style datasets which are suitable for training and benchmarking 3DGS-based pipelines; present case studies in domains such as museums, real estate walkthroughs, and large-scale campus tours; synthesize an evaluation framework that organizes metrics for visual quality, performance, memory usage, and interaction responsiveness. The evaluation framework has four axes: visual fidelity, runtime performance, memory footprint, and interaction responsiveness. Although more recent accelerated NeRF-based systems (e.g., Instant-Neural Graphics Primitives (Instant-NGP) and baked radiance fields) directly reduce the performance difference between 3DGS and explicit methods, 3DGS still provides better trade-offs in interactive tours, such as predictable latency and instant editability. Finally, open challenges in streaming, reflectance, semantics, and authoring ergonomics are outlined, and future directions for 3DGS-based VR tour systems are discussed.
Special Issue: Advancements in AI-Driven Image Processing and Graphics: Techniques, Applications, and Challenges
Comparative Analysis of Loss Functions for Semantic Segmentation: An Empirical Study on Cityscapes Dataset
Semantic segmentation remains a fundamental challenge in computer vision, where the choice and weighting of loss functions significantly impact model performance. This study presents a comprehensive comparative analysis of individual versus combined loss functions with systematic weight ablation for semantic segmentation using modified Attention U-Net and DeepLabV3+ architectures on the Cityscapes dataset. We systematically evaluate seven weight configurations across three loss components (Cross-Entropy, Dice, Focal) through rigorous ablation studies, and validate our findings across two architectures to ensure generalizability. Through extensive experimentation across 20 epochs with 2,975 training and 500 validation images, our results demonstrate that the Dice-dominant weighting configuration (0.5:1.0:0.5 for CE:Dice:Focal) achieves superior performance with 57.83% mean Intersection over Union (mIoU) on Attention U-Net and 58.35% mIoU on DeepLabV3+, representing 7.78% improvement over the best individual loss function. Comprehensive ablation studies reveal that weight configuration critically affects performance, with Dice-dominant weighting consistently outperforming equal weighting (55.59% mIoU) and individual loss functions. Qualitative analysis demonstrates substantial improvements in boundary delineation and small object detection, with boundary IoU improving by 1.41% and challenging class performance (trucks, pedestrians) improving by 5–21%. Statistical analysis reveals that Cross-Entropy provides the most efficient training with a 75.4% loss reduction, while Dice loss exhibits convergence challenges, resulting in only a 34.5% reduction. Our findings conclusively demonstrate that optimized combined loss function weighting achieves better segmentation performance than both individual approaches and naive equal weighting strategies, with consistent improvements across different network architectures.
Domain-Aware Spatial-Temporal Selective Gated LSTM for Real-Time Driver Drowsiness Detection
Driver drowsiness remains a critical cause of road accidents, creating a strong demand for real-time, non-intrusive monitoring systems deployable under practical driving conditions. Vision-based models, particularly CNN–LSTM architectures, have shown effectiveness in temporal behavior analysis, yet they degrade under illumination variation, facial occlusion, and transient noise. This limitation stems from insufficient temporal discrimination and the absence of domain-aware physiological cues, which reduces a model’s ability to capture subtle microsleeps when spatial textures are unreliable. To address these challenges, this study introduces a domain-aware spatial–temporal selective-gated LSTM framework. The model fuses deep spatial representations extracted by ResNet50 with physiologically validated indicators, Eye Aspect Ratio, Mouth Aspect Ratio, and head pitch, forming a unified 2051-dimensional sequence representation. SG-LSTM incorporates two complementary gating modules: a Channel-Selective Gate that suppresses redundant channels and a Temporal-Selective Gate that emphasizes informative time steps, preserving salient micro-expression transitions. Experiments on the NTHU-DDD benchmark, supported by real driving data, show that SG-LSTM achieves 90.09% accuracy and an F1-score of 0.90, outperforming CNN–LSTM and vanilla LSTM baselines. Deployment on an Orange Pi 3 confirms efficiency on resource-constrained edge hardware. The results demonstrate that selective temporal gating combined with domain-aware physiological fusion provides a lightweight yet robust solution for real-time driver monitoring. Future work will explore multimodal sensor fusion and large-scale naturalistic datasets to further enhance generalization.
spatio-temporal micro-expression analysis
Hierarchical Multi-Scale Transformer for Breast Tumor Staging with Visual Interpretability and Risk Stratification
Accurate tumor staging and risk stratification in breast cancer are critical for guiding treatment decisions. Traditional decision-making methods rely on whole slide images (WSIs) analysis, which is labour-intensive and subject to inter-observer variability. To address these challenges in tumor staging, we propose a modular and interpretable deep learning framework for automated tumor staging through multi-resolution histopathological analysis. Our Hierarchical Multi-Scale Transformer (HMS-T) integrates the Vision Transformers (ViTs) operating at 5×, 10×, and 20× magnifications to capture both the cellular and architectural features. In addition, a novel cross-scale attention fusion module combines these multi-scale resolution representations, enabling robust classification according to the AJCC staging system. Trained on 1,092 patients from the TCGA-BRCA cohort, our HMS-T achieves state-of-the-art performance with a staging accuracy of 91.5%, a macro F1-score of 0.89, and a quadratic-weighted kappa of 0.92, which demonstrates the strong agreement with pathological standards. Moreover, our model’s attention maps exhibit high spatial interpretability, aligning closely with expert-annotated regions (Dice score = 0.81). Ultimately, we introduced a lightweight clinical extension for preliminary survival risk stratification, achieving a concordance index of 0.74, thereby bridging the gap toward full prognostic modelling. By combining high performance with transparent decision-making, HMS-T represents a significant advancement toward deployable AI-assisted pathology tools for breast cancer.
An IoT-Enabled Intelligent Data Center Monitoring Framework Using YOLO-Based Image Analysis for Green Cities
This study presents an intelligent data center monitoring and surveillance framework for green cities, integrating Internet of Things (IoT) technology with You Only Look Once (YOLO)-based object detection. The proposed system enhances data center security and operational efficiency by enabling real-time identification of critical components such as servers, routers, cooling systems, and unauthorized personnel. Leveraging computer vision and deep learning, the framework provides proactive monitoring and rapid response to potential threats. The YOLO model is trained on a large, annotated dataset specifically curated for data center environments, allowing precise detection and localization of IoT-based objects of interest. Additional modules incorporating thermal imaging and motion detection further strengthen anomaly recognition and intrusion prevention capabilities. Performance evaluation across multiple data center scenarios demonstrates high detection accuracy, low latency, and strong robustness under varying environmental conditions. The study also highlights the computational efficiency achieved through GPU parallelization and model optimization, ensuring real-time processing in large-scale deployments. Overall, the proposed framework offers a scalable, energy-efficient, and secure solution for smart data center management, contributing to the advancement of sustainable and resilient green city infrastructures.
Object Detection, IoT, Surveillance System, Data Centre, Yolo, green cities
Video Anomaly Classification using Convolutional Neural Network with Bidirectional Long Short-Term Memory using Spatio-Temporal Adaptive Transformer
In smart cities, surveillance systems are extensively deployed to monitor public areas and critical infrastructures. These continuous video streams are the valuable source for analyzing real-time activities and supporting tasks such as object detection, behavior analysis and incident monitoring. Among these applications, detecting unusual or suspicious events is particularly crucial for ensuring safety and enabling timely responses to the threats. Because of its importance in enhancing urban security, video anomaly detection has emerged as a central research focus in the broader field of intelligent video analysis. Thus, this research proposes a new integrated framework of Convolutional Neural Networks with Bidirectional Long Short-Term Memory using Spatio-Temporal Adaptive Transformer named (CNN-BiLSTM-STAT) for the classification of video anomalies in surveillance cameras. The proposed CNN-BiLSTM-STAT was implemented by using different dataset such as UCF-Crime, RLVS, XD_Violence video and RFW-2000. Then, image resizing and label encoding techniques are used in the preprocessing phase to improve the input data. Finally, the proposed integrated classifier CNN-BiLSTM-STAT was used to classify video anomalies into multiple classes. The experimental results demonstrate that the proposed CNN-BiLSTM-STAT method attains an optimal accuracy of 99.91% on the UCF-Crime dataset compared to the existing methods such as recurrent neural networks with LSTM (RNN-LSTM) and Mobile Net.
Logistic Chaos-Based Wild Horse Optimization for Enhanced Color Image Segmentation
Color image segmentation is a crucial step in various image processing applications, playing a vital role in understanding image content. Nature-inspired optimization algorithms have demonstrated significant potential in enhancing segmentation performance. This paper proposes a novel color image segmentation method based on Wild Horse Optimization, enhanced with Chaos Theory, to avoid local optima and accelerate convergence. A Logistic map is integrated into the Wild Horse Optimization to ensure diverse population initialization and to introduce dynamic perturbations during the optimization process. The proposed method is evaluated on various color images. Metrics such as Mean Square Error, Peak Signal-to-Noise Ratio, and Structural Similarity Index are employed to assess the quality of the segmented images. Additionally, the best fitness values, computed using the Davies-Bouldin Index, are analyzed to demonstrate the algorithm’s capacity to achieve optimal segmentation solutions. Comparative experiments with other metaheuristic segmentation methods confirm the superior effectiveness of the proposed Logistic Chaos-based Wild Horse Optimization approach.
Enhancing Short-Film Visual Storytelling with AI-Generated Attention and Focus Maps: A Dual-Map Framework for Data-Driven Analysis
Visual storytelling in short films relies heavily on directing audience attention, yet traditional methods for analyzing visual saliency often lack cinematic context and temporal dynamics. We propose a dual-map framework merging AI-generated attention heatmaps with focus maps to deliver data-driven insights for visual storytelling. The attention heatmap model, trained on cinematic datasets, identifies salient regions while the focus map model, which includes temporal attention gates, captures dynamic shifts in viewer focus. The framework employs spatial clustering to localize coherent areas of interest and quantifies the consistency between predicted attention and actual focus patterns. Furthermore, the system produces annotations that are compatible with standard editing software, which permits filmmakers to improve their work based on empirical evidence. The proposed method fills a crucial void in computational film analysis by merging static saliency with temporal attention modeling, thus creating a tool which connects artistic intent and audience perception. Experimental findings show that the framework successfully identifies gaps between intended and actual viewing behaviors, yielding practical insights for optimizing visual storytelling. This study advances both film studies and human-computer interaction by introducing a scalable, interpretable method for examining and improving cinematic narratives. Merging attention and focus maps creates novel opportunities for data-driven filmmaking, enabling artistic choices to be guided by empirical visual assessment.
ADAPT: General-to-Specific ADAptive Prior Transfer for Hyperspectral Image Reconstruction
Hyperspectral imaging (HSI) captures rich spectral details far beyond RGB cameras. To enable real-time acquisition, contemporary HSI systems often rely on compressive sensing theory, making the reconstruction of hyperspectral data from compressed measurements a fundamental and challenging task in computational imaging. While supervised deep learning has achieved remarkable success in hyperspectral (HS) image reconstruction tasks, the generalization capability of these existing models is often hindered by limited training data, resulting in poor performance on unseen scenes. To address this issue, we propose ADAPT (ADAptive Prior Transfer), a test-time prior transfer framework that bridges the gap between the general priors learned by a pre-trained model and the specific information of a target scene. Our method leverages a pre-trained deep unfolding network and test-time fine-tuning using a loss function derived from the physical CASSI imaging forward model and the emprical prior of HSI. Experiments on the KAIST dataset demonstrated that our method improved the reconstruction quality, boosting the average PSNR from 33.24 to 34.80 dB.
Clustering Websites by Salient Design Features
Designing websites to meet client expectations often involves repeated refinement cycles, which can be both time-consuming and resource-intensive. To address these challenges, this paper presents a versatile, automatic classification system for categorizing real-world websites based on their salient design features, with the goal of supporting automatic website generation. Leveraging Self-Organizing Maps (SOMs) and computer vision techniques, we analyzed website wireframes extracted through a pipeline of image processing techniques, including edge detection, morphological filtering, gradient analysis, and image smoothing. Experiments were conducted on three datasets: manually created wireframes, screen captures of the top 100 websites, and screen captures of the top 1000 websites ranked by SimilarWeb. The analysis revealed seven distinct clusters of website designs: dashboard interfaces, simple information pages, fixed-width product grids, informational pages with sidebars, basic search interfaces, multi-section content layouts, and tabular data interfaces. The classification system successfully grouped websites with similar structural patterns and design features. This research demonstrates that wireframe lines are fundamental elements useful for classification of website designs and that SOM is an effective approach for detecting clusters in website classification. The resulting classification system can provide support for automatic website generation and template creation, potentially reducing the iterative burden of the web design process.
Privacy-preserving Simulation of Facial Palsy Expressions Using Diffusion Models
Facial nerve palsy results in impaired voluntary facial movements, and in Japan its severity is commonly assessed using the 40-point Yanagihara method. However, visual assessment is prone to inter-rater variability, and the use of actual patient images for educational purposes is restricted due to privacy concerns. To overcome these limitations, we propose a simulation framework that produces synthetic facial palsy images by leveraging a Stable Diffusion model with a partially fine-tuned ControlNet. Fine-tuning is confined to timesteps representing higher-level features, which allows the generation of pathological expressions while reducing the likelihood of reproducing patient-specific identity. Furthermore, a preprocessing stage modifies the size and placement of facial components and incorporates contour information from other faces. This design helps keep the fidelity of simulated expressions while maintaining strong privacy protection. The proposed method offers a practical resource for medical training and clinical research, where realistic and privacy-preserving facial data are required.
Astronomical Image Segmentation in Computer Vision for Automated Object Detection and Classification
Automated analysis of astronomical images has become increasingly important due to the growing volume of data generated by modern sky surveys and observatories. This paper presents an image segmentation approach designed to improve the automated detection and classification of objects in astronomical frames. The approach combines classical image processing and modern computer vision techniques to isolate and characterize objects of interest, even in the presence of noise, telescope aberrations, low contrast, or overlapping sources commonly found in wide-field images captured by charge-coupled device (CCD) cameras. The segmentation pipeline employs adaptive thresholding, background subtraction, and morphological filtering to enhance the visibility of both point-like and extended sources. Following segmentation, detected objects are classified based on photometric intensity profiles, shape descriptors, and spatial distribution features. This classification process enables the differentiation between various imaging artifacts and celestial objects, including stars, galaxies, small Solar System objects (SSOs), such as asteroids, comets, and even artificial satellites. The paper describes the modern features for astronomical image processing implemented in the Lemur software within the scope of the Collection Light Technology (CoLiTec) project (https://colitec.space). The Lemur software is designed to perform a sequence of the following main steps: astronomical data mining, classification, background alignment, brightness equalization, image segmentation, typical shape analysis, pattern recognition, object detection and identification, astrometric reduction, photometric reduction, and trajectory detection. The Lemur software has facilitated over 1,700 discoveries of asteroids, including 5 NEOs, 21 Trojan asteroids of Jupiter, and 1 Centaur. In total, it has been used in about 800,000 observations, during which five comets were discovered.
Blood Cancer Classification via Attention-Guided Deep Networks on Microscopic Smear Images
Blood cancer is a type of cancer which has affected many people and their lives have flipped totally while battling the cancer. Most common type of blood cancer is Acute Lymphocytic Leukemia (ALL), Acute Myeloid Leukemia (AML) and Multiple Myeloma (MM). One of primary tests done by pathologist to confirm the diagnosis is microscopic blood smear analysis, which requires human interpretation and consumes a lot of time. To overcome these problems, this proposed study helps them with a distinctive and a novel automated neural network mechanism, called HemoNet, which is a CNN architecture with a CBAM mechanism, incorporated to correctly identify the type of cancer within less time. This study poses as a helping hand rather than completely taking over the human interpretation. The work has been performed over ALL, AML, MM blood smear image dataset. Data transformation was performed to overcome the overfitting, allowing the model to learn the features with different characteristics. The baseline model ‘DeepLeukNet’ which when trained on the dataset, obtained an accuracy of 99.56% with a validation loss of 0.4. Whereas the proposed HemoNet model which has CBAM module, obtained an accuracy of 99.56% with a validation loss of 0.34, and about 79% faster.
Deep learning, image analysis, blood smear image analysis
Copyright (c) 2025 Journal of Image and Graphics
Hybrid Deep Learning Framework for Accurate Surface Defect Detection Using Autoencoder and CNNs
Surface defect detection is paramount in industrial quality control. Conventional methods, which often rely on human inspection or manually engineered statistical models, frequently fail to accurately detect and classify defects, particularly on complex surfaces or those with intricate features. Human inspection is inherently inconsistent and prone to errors due to fatigue, while traditional machine vision systems often lack the sensitivity to clearly identify small or low-contrast defects. This paper proposes a hybrid deep learning framework, termed Autoencoders-Convolutional Neural Networks (AE-CNNs), DefectNet, for surface defect classification, AE, and CNNs to enhance both accuracy and efficiency. The AE is employed to extract and compress reliable features from surface images into latent representations, which are subsequently classified by a CNN enhanced through transfer learning using InceptionV3. The CNN is fine-tuned from a pretrained model with customized fully connected layers to adapt to specific defect characteristics, while the AE is trained exclusively on non-defective images. The encoded features produced by the AE serve as the input to the CNN. The proposed model is evaluated on standard benchmark datasets comprising diverse surface defect types and compared against Anomaly Detection with Autoencoder (ADA), Visual Geometry Group (VGG16), Inception-based Convolutional Neural Network Long Short-Term Memory (In-CNNLSTM), and DTL_Inception_v3. Experimental results demonstrate the superior performance of the proposed method, achieving classification accuracies ranging from 85.60% to 100% across five datasets, including a perfect 100% accuracy on the glass bottle neck dataset.
Hybrid Focal Loss for Accurate Epicardium Segmentation and Mask Generation via Spherical Harmonics
Epicardial Adipose Tissue (EAT) volume has been reported as a clinically relevant biomarker of metabolic risk, yet conventional diabetes assessment relies heavily on biochemical measurements that fluctuate with short-term physiological changes. Computed Tomography (CT)–based EAT quantification offers a more stable alternative; however, accurately delineating the epicardial boundary remains difficult due to its thin, low-contrast structure. Existing Convolutional Neural Network (CNN) approaches trained with Dice-based losses often overlook subtle boundary regions, leading to fragmented contours and degraded downstream EAT estimation. To address this limitation, we propose a Hybrid Focal Loss (HFL) that adaptively balances focal and regional penalties to enhance learning of fine epicardial structures. The loss is embedded in a two-stage framework: Stage 1 performs epicardium segmentation, and Stage 2 reconstructs a smooth mask via spherical harmonics interpolation to enforce global surface consistency. Experimental results on CT images from 15 patients demonstrate that HFL improves segmentation performance by 1.6% in F1-Score compared to Dice Binary Cross-Entropy (BCE) loss and further enhances reconstructed mask accuracy, reducing Hausdorff distance by 6.0 points. When applied to EAT extraction, HFL yields the highest F1 and Intersection over Union (IoU) scores across all evaluated losses, translating into improved agreement with the ground truth of EAT volumes. These findings demonstrate that HFL strengthens both boundary sensitivity and volumetric reliability, suggesting its value for robust EAT quantification and cardiometabolic risk I. INTRODUCTION Diabetes mellitus is a chronic metabolic disorder characterized by sustained hyperglycemia resulting from impaired insulin secretion or sensitivity. Insulin, secreted by pancreatic β-cells, plays a central role in maintaining glucose homeostasis; its dysfunction leads to a range of systemic complications including retinopathy, nephropathy, and arteriosclerosis. Diabetes is typically classified into two major types: Type 1, caused by autoimmune destruction of β-cells, and Type 2, which is predominantly associated with genetic predisposition and lifestyle factors such as diet and physical inactivity. Although clinical diagnosis is conventionally performed using fasting plasma glucose or glycated hemoglobin (HbA1c) levels, these biochemical indicators are affected by short-term physiological fluctuations and comorbidities, thereby reducing diagnostic robustness. Consequently, the identification of more stable, imaging-based biomarkers is essential for reliable metabolic assessment. assessment.
Automatic Detection of Key Fetal Head Anatomical Structures in the First Trimester Using Ultrasound Images
Ultrasound imaging is essential for early pregnancy assessment; however, manual interpretation remains limited by operator dependency and speckle noise. Unlike previous studies that employed You Only Look Once (YOLO)v5, YOLOv6, or YOLOv7, this study introduces an automated approach for detecting key fetal head anatomical structures during the first trimester based on the YOLOv8 architecture. The study systematically compares raw ultrasound images with Hybrid Speckle Noise Reduction (HSNR) preprocessing to evaluate the trade-off between visual enhancement and fine-structure preservation, as quantified by a Structural Similarity Index Measure (SSIM) of 0.912 and a Peak Signal-to-Noise Ratio (PSNR) of 29.8 decibels. Semi-automated annotation with expert validation, yielding a Cohen’s kappa value of 0.84, ensured high labeling reliability. YOLOv8 was trained under multiple configurations, including different optimizers such as stochastic gradient descent, Adam, and AdamW, combined with early stopping and stratified five-fold cross-validation at a resolution of 320 by 320 pixels to balance anatomical detail and real-time efficiency. The optimal configuration using AdamW with early stopping achieved strong detection performance, with a mean average precision at an Intersection-over-Union (IoU) threshold of 0.50 of 0.907 and a recall of 0.857, outperforming models trained on images processed with speckle noise reduction. While speckle noise reduction improved overall image clarity, it slightly reduced the detectability of subtle anatomical features such as nuchal translucency and nasal skin due to excessive smoothing. The proposed model achieved an inference speed of 45 frames per second on a graphics processing unit, demonstrating its feasibility for real-time clinical deployment. Overall, these results highlight the potential of an optimized YOLOv8 model trained on raw ultrasound images as an efficient, reliable, and clinically applicable approach for artificial intelligence–assisted prenatal screening.
Normalized Deep Learning Model for White Blood Cell Classifying
Microscopic particles as leukocytes or White Blood Cells (WBCs) are used in physical immunity. Classifying the WBC into neutrophils, basophils, eosinophils, lymphocytes and monocytes seems so important. In this paper, a Normalized Deep Learning Model (NDLM) is suggested here to classify the WBC types. It has determined structure which is investigated, evaluated and confirmed in this study. Furthermore, extensive comparisons are also taken into accounts. Result of a high accuracy of 96.19% is attained by the suggested NDLM.
Intelligent Image Indexing for Fast, Scalable, and Accurate Visual Information Retrieval
The efficient management and analysis of large-scale digital image collections have become critical challenges across various domains, including photographic archives, e-commerce platforms, and social networks. The effectiveness of image retrieval systems heavily depends on the choice of indexing methods, which influence both the speed and accuracy of visual information retrieval. Among the most prominent approaches, Content-Based Image Retrieval (CBIR) has emerged as a key technique, leveraging digital signatures derived from visual attributes such as color, shape, and texture. This study introduces an innovative CBIR-based automatic image indexing framework designed to enhance not only indexing automation but also retrieval accuracy and computational efficiency. The proposed approach has been rigorously evaluated on multiple image databases and benchmarked against state-of-the-art methods. Experimental results demonstrate significant improvements in retrieval speed, precision, and scalability, making this method a promising solution for real-world applications. By optimizing image management systems in terms of reliability and efficiency, this approach has the potential to transform diverse fields, including digital archiving, online advertising, and social media analytics. The adoption of this advanced indexing paradigm enables businesses and institutions to handle ever-growing volumes of digital images more effectively while ensuring seamless access to relevant visual information for end-users.
Intelligent Detection of Tomato Leaf Diseases: An Explainable Deep Learning Approach
Plant diseases are one of the most significant threats to global food security, impacting food availability and safety, as well as their negative impact on agricultural productivity. Addressing this major challenge requires the development of advanced and modern disease diagnostic equipment that not only detects but also accurately identifies disease. In this paper, we propose an innovative algorithm based on transfer learning for the detection of tomato leaf diseases. Convolutional neural networks (CNNs) were used in conjunction with pre-trained models such as EfficientNetB3, Xception, and MobileNetV2 to accurately classify and diagnose diseases. Experimental results demonstrated high classification accuracy, reaching 0.93 for CNN, 0.94 for MobileNetV2, 0.95 for Xception, and 0.995 for EfficientNetB3. Using machine learning and image processing techniques, the system can quickly and automatically diagnose disease symptoms on plant leaves, providing farmers with up-to-date and reliable information on what to do. The research also addresses the interpretability of the model by applying Saliency Maps and Grad-CAM techniques to visually explain the model's decisions, which enhances transparency and increases user confidence in the AI-based system in the agricultural field.
: Machine learning techniques ,Convolutional neural network, Image diagnostic, Saliency Maps, Grad-CAM, explainable AI, Leaf Disease Detection
Comparative Evaluation of Machine Learning Models for Hand Gesture Recognition Using Feature Extraction
Hand gesture recognition (HGR) is essential for enabling natural interactions between humans and machines. Nevertheless, achieving accurate recognition remains challenging, particularly when employing lightweight machine learning models with classical feature extraction. This study evaluates the effectiveness of Moment Invariant features combined with three classifiers—Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbor (KNN) for gesture classification. A dataset of 1,200 Evaluation results show that SVM achieved a perfect training accuracy of 99.83% and a test accuracy of 81.42%. The Decision Tree algorithm followed closely with a training accuracy of 99.25% and a test accuracy of 77.25%, while KNN recorded 99.08% for training and 98.75% for testing and 78.00% for training. These findings demonstrate the capability of Moment Invariant features to capture discriminative patterns in gesture data and highlight the superiority of SVM for robust and efficient recognition. The proposed approach is promising for real-time gesture-based systems operating in resource-constrained environments without relying on deep learning architectures. This work contributes to the development of efficient and accessible HGR systems, particularly in low computation environments
Decision Tree; Feature Extraction; Hand Gesture Recognition; Human-Computer Interaction; K-Nearest Neighbor; Machine Learning; Moment Invariant; Support Vector Machine
A Hybrid Deep Feature Extraction Framework with Quantum Grey Wolf Optimization for Enhanced Content-Based Image Retrieval
A key tool for organising and extracting visual information from large picture databases is content-based image retrieval (CBIR). To make CBIR systems much more accurate and efficient, this research proposes a new combined approach that uses deep feature extraction along with Quantum Grey Wolf Optimisation (QGWO). The suggested system captures complex visual patterns across a variety of picture categories by utilising pre-trained Convolutional Neural Networks (CNNs) for reliable and advanced feature extraction. This paper presents the retrieval of single objects and multi-objects. The deep learning techniques utilised in this work are Inception V1, Inception V2 and ResNet50, which help in the extraction of feature vectors. To make the process faster and more effective, these features are improved using QGWO, a method that combines ideas from quantum computing with the social behaviour and hunting strategies of grey wolves. Our combined method performs better than existing CBIR algorithms in precision, recall, accuracy, and F1 scores, based on thorough testing with the Corel 1K image dataset. The fusion of deep learning with a quantum-inspired optimisation approach resulted in a retrieval accuracy of 99.25%, an F1 score of 94.56%, and an error percentage of 1.08%. The results prove the computational efficiency of contemporary CBIR systems.
Ensemble Transfer Learning Approach for Efficient Brain Tumor Prediction
— In medical imaging, brain tumor detection and classification is considered a critical task. Applying convolutional neural networks (CNNs) to efficiently classify multiple types of brain tumors showed superior potential in helping physicians to provide accurate diagnosis. MRI imaging and X-ray scans provide the source of samples needed to train and test CNN models. In this paper, we propose a new hybrid deep learning based methodology to achieve high classification accuracy on large brain tumor dataset. Our method uses four well-known pre-trained models: EfficientNetB07, MobileNetV2, Xception, and VGG-16, and combine them after applying transfer learning to each model. After that, dense layers are added along with flatten and dropout before using the softmax layer to perform the classification. This new approach was applied on a dataset named: Kaggle 7023 which consists of four classes. Results show that the combination of these four models achieve a high prediction accuracy of 99.62% which is better than using each standalone model. Comparison has also been down with previous methodologies on the same dataset.
Keywords— Brain tumor ,Artificial intelligence, Diagnosis, Medical imaging.
Nose-Lip and Background region segmentation for precisely predicting face orientation
The core necessity for the video analysis-based application is the prediction of human face orientation. Here we propose a unique and novel solution for predicting the face orientation through a combination of facial features like the nose and lip region along with non-facial features like the background region of the facial image. The proposed solution has two stages. The first stage, namely the Nose-Lip Segmenter(NLSeg) generates Nose-Lip mask(m2) through a custom-trained U-Net model. The U-Net model segments the nose and lip region very accurately with an intersection over union (IOU) score of 87.56%. The second stage generates Background region mask(m1) through two different methods. In the first method, a Background region mask(m1) is constructed through the k-means clustering and HSV (Hue-Saturation-Value) skin color model. In the second method, the Background region mask(m1) is constructed through the MediaPipe selfie segmentation model. The proposed algorithm blends the Nose-Lip mask(m2) and Background region mask(m1) together to predict face orientation across left, right, and neutral directions. The efficiency of both approaches, namely (NLSeg+method1) and (NLSeg+method2), is studied and evaluated on five public datasets namely BIWI-Kinect, AFLW2000-3D, Pandora, Pointing04 and Stirling/ESRC 3D face . The proposed work yields very accurate results in spite of challenges like diverse head pose angles, facial expressions or improper segmentation. The accuracy is 98% when subjects are non-occluded and is above 90% in the case of partial occlusion.
Edge detection using clip Relu-based enhanced Hybrid Network
Edge detection is a crucial task in computer vision, serving as a foundation for applications like object detection, segmentation, and scene understanding. Traditional edge detection methods often fail to capture complex boundaries in natural images. This study proposes a novel deep learning-based architecture called Clip-MobileNet-U-Net, which integrates the lightweight and efficient MobileNet encoder with the segmentation power of U-Net and the stabilising capabilities of the Clip ReLU activation function. The MobileNet backbone significantly reduces computational cost and model size, making the architecture suitable for edge detection in resource-constrained objects such as mobile and embedded devices. The Clip ReLU activation, a clipped version of the standard ReLU, is employed throughout the network to ensure bounded activations, which helps to avoid gradient explosions and stabilises training. This modification improves model generalisation, reduces overfitting, and enhances edge sharpness by preserving fine-grained features. The U-Net decoder structure with skip connections allows the model to recover spatial details lost during downsampling. The proposed Clip-MobileNet-U-Net has been trained and assessed on the BSDS500 dataset using 3200 training images, 1000 validation images, and 500 testing images. Data augmentation techniques, including rotation, have been applied to enrich training diversity. The model achieved competitive performance with mDC of 0.9256 and mIoU of 0.8419, outperforming traditional U-Net and other deep architectures in edge detection. Clip-MobileNet-U-Net offers a vigorous, precise, and scalable solution for edge detection, combining low computational complexity with high accuracy, making it outstanding for real-time and practical computer vision applications.