Journal of Image and Graphics aims to publish original, previously unpublished, research, reviews, communication, plus case studies and short research notes, on both applied and theoretical aspects in imaging and graphics research.
An Approach to the Problem of Calculating Target Scores Using Camera to Support Shooting Training
Image processing methods are now widely used in the world in civilian fields such as handwriting recognition, face recognition, gender recognition, self-driving cars, disease diagnosis, etc. The article introduces a target reporting method using machine learning algorithms and image processing methods to identify bullet marks on targets, supporting shooting training. Bullet mark images taken by camera will be digitized and the K-nearest neighbor machine learning recognition algorithm will be applied. The algorithm models after being trained give quite accurate results in identifying bullet mark locations in the target mark circle, thereby calculating the total score achieved by the shooter after shooting, contributing to improving shooting training capacity.
Machine learning recognition, image processing, target score reporting
Copyright (c) 2025 Journal of Image and Graphics
Deep Learning Model for Automated Heart Disease Diagnosis Using ECG Signal
The development of technology improves the early detection of disease and diagnosis in the field of medical. Most of the people suffer with heart disease depending on the age factor and is millions of death cases are recorded due to heart attacks. In this paper heart rate variability (HRV) is measured for early detection of cardio vascular disease (CVD). HRV analyse the heartbeats and evaluated the time intervals between the consecutive heart beats. The CPSC 2018 dataset is considered in this paper to evaluate the ECG signals. Initially the R-R intervals are evaluated. The key features of the HRV like spatial, frequency, Poincare are extracted and a deep learning model Efficient-Net b0 (ENb0) is used for classification and identification of heart disease. The ROC curve and confusion matrix is achieved using the deep learning classification model. The parameters like accuracy, precision, recall and F1 score are evaluated using the proposed deep learning model and compared with other techniques. The ENb0 model achieves better efficiency in parameters evaluated and is effective in early detection of CVD and helps to support the research in the field of medical.
Binomial Dropout Convolutional Neural Network Classifier-Based Micro-Expression Recognition System
Facial expression recognition is essential for a variety of applications, including marketing, commerce, security systems, and psychotherapy. The two primary categories of facial expressions are macro-expression and micro-expression. When comparing macro expression, it can be difficult to recognize micro expression in real time because it typically happens in high stakes scenarios. So, in this work, auto-detection of facial micro expressions was proposed by using a Binomial Dropout Convolution Neural Network (BDCNN) classifier technique. Initially, the input video collected from the publicly available source is converted into frames and the keyframes are generated by using the Distance Deviation-based Rood Pattern Search algorithm (DDRPS). Then, the keyframes are preprocessed to reduce the significance of illumination, face detection, landmark points on the face, and motion magnification. Afterward, the important features are extracted from the preprocessed frames. Finally, the BDCNN classifier was used to recognize the micro-expression. The average accuracy and F- Measure values obtained on the SAMM data set are 0.9758 and 0.9247; also, for the SMIC dataset, the values are 0.8989 and 0.8536, similarly, for the CASME II dataset, the accuracy and F-Measures are 0.9784 and 0.9509. The proposed classifier is compared to state-of-art methods to prove its superiority.
Topic: Machine Learning Based Techniques for Image and Video Processing
CLDS-YOLO: Corn Leaf Disease Detection and Severity Evaluation Using YOLOv9
Corn is one of the main crops around the world, especially in the Philippines, where agriculture is threatened by diseases such as Northern Corn Leaf Blight, Gray Leaf Spot, and Corn Rust. Tropical cyclones and environmental variables worsen these illnesses, resulting in dramatically lower crop yields and quality. This study introduces CLDS-YOLO (Corn Leaf Disease Detection and Severity Evaluation Using YOLOv9), which utilizes YOLOv9 instance segmentation for high-precision disease detection and integrates fuzzy logic for severity evaluation with relative leaf area (RLA) computation and the number of diseased regions. YOLOv9e-seg achieved near-perfect precision for leaf detection (approximately 1.0), but showed challenges in identifying diseased regions, with lower precision ~0.7 and recall ~0.3 for segmentation tasks. Despite these limitations, the model's boxing tasks demonstrated higher performance, reflected in mAP@0.5 and F1-scores. Severity analysis successfully classified disease levels, facilitating effective crop management. This study highlights the potential of CLDS-YOLO in real-time disease detection and severity evaluation, offering a foundation for an indoor planting system that ensures consistent crop health monitoring and protection from adverse weather conditions.
WGEF: An Optimized Deep Learning Model for Recognition of Road Surface Condition Using SVM Classifier
Road surface often plays a crucial role in day-to-day scenario and a major source of transportation. The dependency of public, government, and other applications on road is major issue. The roads frequently get damaged due to the weather conditions. Different weather conditions play different role on the road surface. Identification of road conditions is getting difficult. To overcome the problem, in this paper proposed a deep learning (DL), Optimization and machine learning (ML) model and combine both the models for recognition and classification of Road surface conditions (RSC). The model is designed on publicly available dataset. The images in the dataset are processed using wavelet transform (W) and extracted GLCM (G) data points. The detailed feature extraction is performed using Efficient-Net (E) model. The obtained data points are optimized using firefly optimization (F) and finally classified using support vector machine (SVM). The identification of RSC is performed using low number of data points and achieved best result. The combination of DLML provided better results in term of metric evaluated like sensitivity, specificity, precision, and accuracy. The rate of accuracy achieved using the proposed design is 98.15%.
Performance Evaluation of Different Optimizers on Alzheimer’s Disease Classification using a Customized Convolutional Neural Network
Alzheimer's disease is a type of dementia that usually affects elderly people. It is a neurological disorder that causes a patient to lose memory gradually over time. The brain of an Alzheimer disease (AD) patient shrinks due to the accumulation of amyloid plaques in between the neurons. As a result, the neurons, which are the basic building blocks of the brain, lose connections and cannot communicate with each other. A person can be prevented from having AD if diagnosed at the right time. So it’s very important to detect patients with mild symptoms of dementia in order to save them from getting AD. In this work, we have proposed a customized CNN model for classifying Alzheimer’s disease. The model has been evaluated with two benchmark datasets, the Kaggle Alzheimer’s dataset and the ADNI dataset. The two datasets differ in the number of images. The K-fold technique has been applied to overcome the problem of class imbalance. We have updated the model parameters using optimizers, namely SGD, SGD with momentum, AdaGrad, AdaDelta, RMSprop, and Adam. Experimental results established that the proposed model outperforms many of the state-of-the-art models, considering the two benchmark datasets.
Alzheimer disease, CNN, Class imbalance, K Fold, Optimizer
A Comparative Study of Convolutional Neural Networks (CNN) Architectures on Microscopic Blood Film Images for Malaria Diagnosis
Malaria has been recorded as one of the deadliest diseases globally. Accurate diagnosis is essential for suitable treatment, and the traditional practice of malaria diagnosis has proved inefficient as results depend on the skills of the health personnel. In recent times, deep learning models have proved helpful in the quick detection of malarial parasites. This research focused on developing a classification model of Convolutional Neural Networks (CNN) architectures and comparing these models to identify the most effective one for automatic malaria parasite detection on thin blood smear images. A dataset of 27,558 digital blood images was collected from the National Institutes of Health (NIH) database in Bangkok, Thailand. The dataset was categorized into parasitized and uninfected cells and was fragmented into training (80%) and validation (20%) sets. Performance metrics for measuring the model's performance include sensitivity, specificity, precision, and F1 Score. The model predicted and classified thin blood smear digital images as either parasitized or uninfected with custom InceptionV3 outperforming the VGG19 and custom CNN with an accuracy of 89.85%. The result shows that malaria diagnosis on microscopic thin blood images using deep learning can potentially improve early detection of malaria parasites, which could prevent deaths, reduce the workload of parasitologists, and eliminate other limitations of the traditional malaria diagnostic approaches.
Convolutional Neural Network, malarial parasite detection, classification model, Diagnosis, Digital blood images.
Robotic Arm: Cutting Edge Sorting Machine for Industrial Optimization
This paper introduces an innovative Sorting Robotic Arm (SRA) utilizing a SCARA (Selective Compliance Assembly Robot Arm) model for hardware and imposing the advanced object detection capabilities of the Grounding Dino model, enabling real-time identification and sorting of objects with exceptional accuracy and speed. The modular design of this arm facilitates easy customization and scalability, allowing for seamless deployment in various industrial settings. By harnessing the capabilities of SCARA robotics and advanced AI. This paper aims to revolutionize sorting processes, improving productivity, accuracy, and efficiency in industrial operations including recycling facilities, logistics and warehousing, seed sorting in agriculture.
Special Issue: Advancements in AI-Driven Image Processing and Graphics: Techniques, Applications, and Challenges
Performance Comparison Analysis of Random Forest, Support Vector Machine, and AdaBoost in Arrhythmia Classification
Arrhythmia is a condition characterized by irregularities in heart rhythm, where the heartbeat may be too fast, too slow, or irregular, potentially leading to severe complications such as heart attacks or sudden cardiac death. Accurate diagnosis of Arrhythmia is essential but traditionally relies on Electrocardiogram (ECG) analysis by medical experts, which can be time-consuming and subject to variability. In recent years, computational methods have gained prominence in arrhythmia classification, improving diagnostic speed, Accuracy, and automation. This study investigates the effectiveness of machine learning models, namely Random Forest (RF), Support Vector Machine (SVM), and Adaptive Boosting (AdaBoost), in classifying arrhythmias using features extracted from ECG signals through Discrete Wavelet Transform (DWT). The dataset is sourced from the MIT-BIH Arrhythmia Database, and the research involves several stages, including data collection, preprocessing, feature extraction, model training, and performance evaluation. The results indicate that Random Forest achieves the highest Accuracy at 97.6%, Support Vector Machine at 97.2%, and AdaBoost at 90.2%. These findings demonstrate the superior performance of Random Forest in handling arrhythmia classification tasks, highlighting its potential for enhancing automated ECG interpretation and assisting in early diagnosis and clinical decision-making.
Optimized Heart Disease Image Classification on Edge Devices using Knowledge Distillation and Layer Compression
The high number of edge devices enables remote, real-time diagnostic testing, particularly in remote areas. Convolutional neural network models (CNNs), in particular, are excellent at analyzing CT scans and other medical images and CXRs for heart diseases, but their computational complexity usually makes them unsuitable for usage on edge devices with limited resources. This paper presents a new compressed layered knowledge distillation model for precise medical image diagnosis, for example, SARS-COVID-19 and other heart diseases. We utilize knowledge distillation to transfer the teacher network's knowledge to a smaller, compressed student network for edge deployment. Moreover, we utilize a well-structured layer compression approach, with a focus on decoupling and merging and not pruning, to optimize the student network architecture. Two data sets, Chest CT-Scan and SARS-CoV-2 CT-Scan, were used to test the suggested model. When compared to the existing models, our performance is superior in the following ways: 98.93%, precision 98.41%, recall 98.69%, F1-score 98.44%, MSE 0.04, RMSE 0.16 for the CT-Scan data set and SARS-CoV-2 and 98.25%, precision 98.78%, recall 98.86%, F1-score 98.14%, MSE 0.09, RMSE 0.13 for the Chest CT-Scan data set. These results verify the efficacy of our intended technique for achieving high diagnostic accuracy at low error in edge devices.
, heart disease diagnosis, deep learning compression
Comprehensive Analysis of Feature Detectors and Descriptors for Stitching UAV Images with Perspective Distortion
This study analyzes traditional and deep-learning-based feature detectors and descriptors for UAV image stitching under perspective distortion. Traditional methods, including SIFT, SURF, and AKAZE, achieve high keypoint detection and matching accuracy but struggle with geometric distortions, leading to artifacts such as ghosting and misalignment in complex UAV scenes. Deep-learning-based approaches, such as SuperPoint, ALIKED, and DISK, offer superior alignment accuracy and visual coherence by detecting fewer but more robust keypoints. DISK and ALIKED, in particular, demonstrate high spatial consistency, reducing perceptual artifacts as validated by PIQE, NIQE, and BRISQUE metrics. The results indicate that deep-learning-based detectors outperform traditional methods in UAV image stitching under perspective distortions. Future work will explore hybrid models that combine the strengths of both approaches to enhance stitching accuracy and computational efficiency.
CenterFormer: Coupling CenterNet and Vision Transformer for Accurate Wheat Head Detection
Wheat is a staple crop cultivated widely across the world, making effective management of wheat fields a critical task. A key component of this management is accurately identifying and counting wheat heads, which provides essential data for assessing growth conditions, estimating crop yields and optimizing agricultural. This study introduces a novel approach for automatic wheat head detection by treating the wheat head as a single point to avoid ambiguous annotation of dense objects while leveraging the long-range dependency modeling capabilities of Transformer architecture to learn multi-scale features for head prediction, dubbed as CenterFormer. Specifically, we employ a hierarchical Transformer architecture with self-attention exploitation in both spatial and channel domains as the backbone to extract multi-scale features in the hierarchical stages. To maintain the linear complexity of the Transformer block, we implement window-based self-attention in spatial domain and group-wised self-attention in channel direction. In addition, to leverage the multi-scale features with both detailed spatial information and abstracted semantic contexts, we design a simple yet effective fusion block to integrate these features for enhanced wheat prediction. The prediction block aims to estimate a heat map, denoting the probabilities if the points are located at the centers of the wheat heads, and regresses other object properties such as size and sub-pixel deviations for each center location. Extensive experiments on the Global Wheat Head Detection (GWHD) dataset have demonstrated that our proposed method achieves substantial performance improvements compared with the state-of-the-art object detection models.
Wheat head detection, Transformer, multi-scale feature fusion,
Fine-Tuned Object Detection for Mask Recognition Using Green Computing in IoT Systems
This study proposes a sustainable and intelligent approach to face mask detection, a critical component for ensuring public transportation safety within IoT environments. By fine-tuning an object recognition model, the system is designed to accurately detect individuals wearing face masks using energy-efficient, or "green," computational methods. The research leverages a comprehensive dataset on road accidents and vehicle information, analyzing factors such as fatalities, vehicle movement patterns, driver gender, trip purposes, road types, and days of the week. Advanced data analysis and visualization tools—including bar charts and pie charts—are employed to highlight significant patterns and insights. A key aspect of the study involves exploring the correlation between accident occurrences and vehicle manufacturers, aiming to uncover potential causative relationships. The integration of green computing techniques ensures efficient handling and processing of large-scale IoT data while optimizing the detection model’s accuracy and performance. This research contributes to the broader domain of Advancements in AI-driven Image Processing and Graphics: Techniques, Applications, and Challenges, particularly in the context of public safety and smart transportation systems. The proposed face mask recognition model is trained on relevant datasets and optimized to perform reliably in real-world scenarios. The findings not only emphasize the importance of monitoring passenger mask usage but also provide actionable insights for transport authorities and policymakers. By analyzing trends and behavioral patterns, this work supports the development of more effective safety regulations and promotes the integration of AI-driven, sustainable solutions into IoT-based public transportation systems.
Mask Detection, IoT, Object Detection, integration, traffic monitoring infrastructure, compatibility, RCNN, Face Mask Detection, data exchange, Green Technology
Performance Comparison of YOLOv8, YOLOv5 and YOLOv11 in Nature Image Segmentation
Image Segmentation is one of the important tasks in computer vision techniques. This technique is a basic technique for partitioning image into detailed segments that enable the analysis and retrieval process. This paper discusses the performance of the three YOLO models, especially YOLOv5, YOLOv8, YOLOv11 in nature image segmentation by focusing on reptile images. Of course, with a diverse dataset of reptile images taken under different environmental conditions, this experiment accuracy, precision, recall, Mean Average Precision (mAP), and Computational Efficiency. From the experiments carried out, we carried out comparative tests related to the three models and produced different outputs. These three models of course also have one of their advantages, which shows the best performance of each model. YOLOv5 is a model that is easy to implement, YOLOv8 can approach without using anchors, and YOLOv11 has a higher efficiency aspect than the two models. The final results show that YOLOv11 introduced significant improvements in architecture and training methods, making it a versatile choice for a variety of computer vision tasks.
Efficient Few-Shot Image Classification with Unbalanced Sinkhorn Distance for Robust Feature Alignment and Large-Scale Applications
The core challenge of few-shot image classification lies in efficiently learning and accurately classifying with limited labeled data. Current metric-based meta-learning methods primarily rely on computing the structural distance between the query and support sets for matching. However, traditional metric learning typically employs Euclidean distance or standard Sinkhorn distance for feature matching, assuming that the total mass of the source and target distributions is equal. This assumption overlooks the imbalance in data distribution in real-world tasks, leading to reduced matching efficiency. Additionally, intra-class feature distribution imbalance can further impact the accuracy of metric learning. To overcome these challenges, this study systematically introduces Unbalanced Sinkhorn Distance (USD) into few-shot learning to enhance the model’s ability to match features under imbalanced distributions. USD allows dynamic adjustment of matching distributions during metric computation, making it more adaptable to real-world data imbalances. Moreover, this method effectively reduces the interference of background noise when matching features between the support and query sets while maintaining low computational costs, thereby improving both classification accuracy and efficiency. Experimental results demonstrate that our method achieves state-of-the-art performance on four few-shot learning benchmark datasets: Mini-ImageNet, Tiered-ImageNet, FC100, and CUB, significantly outperforming existing optimal transport (OT)-based methods.
Evaluating Facial Emotional Proportion Based on Computer Vision Technique
Emotion detection is a technique to recognize human emotions by addressing facial expressions. It is essential for psychology, security systems, and human-computer interaction. The ability to perceive and interpret an individual’s facial expressions helps to understand their actions and improve the interaction between a person and a computer. Facial emotion recognition (FER) is instrumental whenever there is a need for human-computer interaction for behavioral assessment, like in clinical usage. When using machine learning models in the Face Emotion Recognition (FER) field, the accuracy and robustness remain difficult because of the diversity of human faces and image changes, such as differences in spatial pose and lighting. This research used the FER2013 dataset, which contained approximately 30,000 images divided into seven classes (anger face, disgust face, fear face, happy face, sad face, surprise face, and neutral face). It also used two CNN models (VGG19 and Sequential). The result of the VGG19 model achieved 67% accuracy, validation accuracy achieved 64%, the sequential model achieved 77% accuracy, and validation accuracy achieved 66%.
Vision Transformer for the Categorization of Breast Cancer from H&E Histopathology Images
Breast cancer (BC) is the most frequent form of cancer, accounting for 24.5% of all cancer cases worldwide, with projections estimating 364,000 cases by 2040.Accurate diagnosis and effective categorization of BC are essential for proper treatment planning, patient management and improved survival.Traditionally, pathologists examine histopathology specimens manually using a microscope to categorize the BC, which is labor-intensive, time-consuming, prone to subjectivity, and constrained by the availability of experts. An automated approach can address these limitations, but previous automated methods failed to achieve sufficient accuracy and reliability. This study presents an automated BC categorization method leveraging whole slide histopathology images and a transformer-based deep learning model. The proposed method uses a cascade of transformers to classify BC using 40X histopathology images, following the taxonomy defined by the BRACS dataset, distinguishing between benign, atypical, and malignant cases. First, it classifies BC into three primary categories—benign, atypical and malignant—and subsequently determines the specific sub-types within each category. The proposed method was validated using two widely recognized datasets: BRACS and BreakHis. On BRACS, it achieved 95.6% accuracy in classifying BC into benign, atypical, and malignant categories, with sub-type accuracies of 94.7% for benign, 98.6% for atypical, and 99.1% for malignant cases. On the BreakHis dataset, the model achieved 93% accuracy for binary benign-malignant classification, with sub-type accuracies of 94% and 91% for benign and malignant cases, respectively. Proposed method outperformed existing methods in accuracy and robustness, making it a promising tool for automated BC diagnosis and classification.
breast cancer, whole slide image, vision transformer, histopathology image, cancer classification
Evaluation Study for the Performance of PSO and AOPSO Algorithms in Optimizing the SVM Classifier
Face recognition plays a crucial role in our daily lives by identifying and authenticating goals. One of the most common face recognition approaches is the support vector machine, which is a supervised machine learning method. The present face recognition methods have utilized PSO and AOPSO approaches for optimizing the training parameters in SVM. This paper is a comparative study of PSO and AOPSO algorithms that are used as a face recognition model with SVM (PSO-SVM and AOPSO-SVM). The suggested methods are implemented and evaluated on two human faces datasets: CASIA V5 and FEI Face datasets. The proposed methods are applied for ten rounds using n-fold cross-validation. Three different metrics are used to evaluate the PSO and AOPSO algorithms and assess their performance: accuracy results, computational time and to assess their optimization through two optimization functions. The overall comparison results demonstrate that the AOPSO outperforms the standard PSO algorithm in terms of accuracy and computational time for CASIAV5 and FEI Face datasets. Moreover, the AOPSO is better than the standard PSO algorithm for most rounds using the optimization functions based on their fitness values.
Research on Controllable Image Generation Technology for Chinese Text
With the rapid development of artificial intelligence technology, text-generated image technology has attracted wide attention, which shows great potential in enhancing human-computer interaction, enhancing the credibility of visual content and creating novel works of art. In this paper, an improved generative antagonistic network model based on attention mechanism, Improved AttnGAN, is proposed to deal with the challenges of existing technologies in dealing with complex text input, improving image clarity and authenticity, and enhancing semantic consistency between text and image. By introducing the SimAM attention mechanism and optimizing the AttnGAN architecture, the model significantly improves the quality and diversity of image generation. The experimental results show that, The Improved AttnGan model is superior to StackGan, DM-Gan, MirrorGan, DF-Gan, AttnGAN and other models. Improved AttnGAN has obvious advantages in terms of image quality and realism.
A Novel Deep Learning Approach for Speckle Denoising using Hyper-parameter Tuning
Ultrasound imaging is one of the key noninvasive diagnostic methods used in medicine today. Many of the deep learning (DL) speckle denoising algorithms, in particular Autoencoder models and Convolutional Neural Network (CNN) based techniques, tend to be overfit, have low accuracy, or even perform badly on different sets of data. To help tackle these problems, the study suggests a new Unet-Elu CNN architecture that incorporates an Exponential Linear Unit (ELU) as its activation function. ELU is also used to endow the model with non-linearity while facilitating the flow of gradients within the model. The batch normalization and dropout layers are added with focus on improving accuracy and preventing overfitting. The proposed framework is evaluated in two stages. In stage 1 the proposed framework is compared with fine-tuned state-of-the-art UNet, UNet_ReLU, CNN Autoencoder and other filtering methods. For stage 2 comparative analysis transfer learning models are optimized and compared. The proposed framework performs without any sign of performance degradation and overfitting when tested on different datasets. This model was evaluated using the evaluation metrics of PSNR, SSIM, and MSE with different levels of speckle noise in order to determine the effectiveness of these techniques. It was able to achieve a PSNR of 37.76 dB and SSIM 98% for the Unet-Elu CNN model which indicates a strong denoising performance. The optimized adjustment to the architecture and ELU activation function of the Unet-Elu CNN model marks a significant improvement in ultrasound image denoising.
Deep Learning, Speckle Reduction, Ultrasound Imaging, Denoising, Improved Framework
Dual-stream generative network based staining transfer for biomarker in breast cancer
Pathological examination is a crucial standard in cancer diagnosis, with breast cancer being one of the leading causes of morbidity and mortality in recent years, posing a major threat to health. Enhancing pathological examination capabilities has become an important way to save lives and improve patients' quality of life. Common pathological examination methods include Hematoxylin and Eosin (H&E) and Immunohistochemistry (IHC) staining. H&E-stained images alone are often insufficient for cancer diagnosis, while IHC provides more comprehensive information for confirmed diagnosis. To address the challenges of limited IHC resources and high-cost consumption, we aim to generate virtual IHC images from H&E-stained images. In practice, it is difficult to perform multiple stains on the same tissue section, making it hard to obtain pixel-level matched data. To overcome this, we propose a dual-stream generative network that leverages pathological consistency constraints and a pathological representation network to extract pathological information and improve prediction accuracy. The network also incorporates structural similarity constraints and skip connections to enhance structural similarity. Additionally, we use stain unmixing results as annotated data, significantly reducing the workload of pathologists. Extensive experiments demonstrate that our method exhibits superior stability and performance compared to existing approaches.
Dynamic Attention for Enhancement of Weak Contrast Images using Advance ASENet
Low-light photography frequently results in photos with noise and inadequate brightness. Therefore, one of the most difficult tasks in computer vision is improving images in low light. Numerous techniques have been put forth for this purpose, however they frequently exacerbated the underlying noise in the input image and failed in extremely low light conditions. This research proposes the Advanced Attention-Shift Enhancement Network (Adv-ASENet), a promising and innovative techniques for effectively improving weak contrast low light images (WCLL), as a solution to this challenging challenge. By using an attention mechanism to selectively focus on the most significant details in a low-light image, this deep learning technique enables the model to prioritise enhancing certain regions while minimising noise amplification in well-lit areas, producing an enhancement result that is more balanced and inherent. Because poor contrast photos frequently need targeted improvement in certain places where contrast is absent, including low light areas, shadows, or edges, this method can be very successful. Localised problems, including poor contrast in certain regions, are common in weak contrast photos. These regions can be selectively enhanced by ASENet's dynamic attention blocks without overprocessing sections that are well-contrasted. The spatial attention module preserves well-exposed sections of the picture while localising enhancement to areas that need contrast boosting. According to the experimental results, the suggested WCLL image enhancement network maintains an appropriate level of network complexity while producing adequate results when compared to current techniques in terms of metrics like SSIM, PSNR, MSE, Entropy, etc.
Analysis Evolution of Image Caption Techniques: Combining Conventional and Modern Methods for Improvement
This research aims to study the challenges facing artificial intelligence in the field of generating image captions, a complex process that requires effective integration between computer vision algorithms and natural language processing models to generate accurate and understandable sentences that correctly reflect the image content. Two main approaches are analyzed: traditional methods such as retrieval and fixed templates, and modern frameworks that include encoder-decoder, attention mechanisms, and advanced training techniques with their effectiveness and accuracy evaluated. The results show that both conventional and modern methods have their advantages, as modern methods have outperformed and contributed to significantly improving the quality of annotations, and opportunities for integration between the two methods can be achieved by combining them, which enhances the possibility of improving the performance of models in general. The study recommends further research to develop strategies that overcome the current limitations and improve performance.
Image caption, Conventional methods Modern methods
Integrating Spatial Pyramid Pooling for Multi-Scale Brain Tumor Classification in Deep Learning
Deep learning has transformed medical image analysis; in particular, many different brain tumors are being produced. Accurate detection is crucial for effective treatment and contributes to prolonging the life of patients. While MRI is a standard diagnostic tool, manual interpretation can be slow and sometimes error prone. As such, automatic classification systems based on Convolutional Neural Networks (CNNs) are gaining importance. However, standard CNNs are generally good for capturing only the continuous features within one region of the data, and that means they need huge amounts of training samples to work well in practice. We also struggle to capture diverse shapes, sizes and positions of brain tumors. To meet this challenge, our paper proposes the SPP-MobileNet model, which integrates a Spatial Pyramid Pooling (SPP) block into the MobileNet architecture. With the SPP layer for multi-scale feature extraction, our classifier is much better at spotting tumor appearance changes without resizing images. By building this into MobileNet, SPP-MobileNet maintains all that model's computational efficiency while boosting classification accuracy. On two MRI datasets, the proposed model outperformed other state-of-the-art methods with an accuracy of 98.86% and perfect precision. Its recall rate was 97.68%, while the Matthews Correlation Coefficient value reached 97.75 %. These results suggest that SPP-MobileNet is a powerful tool for brain tumor classification, and it should go some way to improving diagnostic accuracy and speed. In the future, we will focus on tuning the model for more complex types of brain tumors and applying it across various other medical imaging tasks.
Development of preprocessing stage for early cervical cancer detection using UNET Diffusion model
Colposcopy, a medical procedure for examining the cervix, is often used for diagnosing cervical cancer. It generates medical images often containing bright regions known as specular reflections (SR). These bright areas, caused by factors such as moisture and device lighting, can affect further image analysis steps like feature extraction and classification. We employed a two-stage process, detecting the specular reflection and inpainting the detected regions in the colposcopy images. The first stage creates SR region masks using local thresholding and white top-hat morphological operations on augmented and pre-processed images. The second stage employs Stable Diffusion v1-5, a UNET diffusion model which we additionally train on our dataset of colposcopy images using Dreambooth to inpaint the SR regions detected in the first stage. The inpainting workflow is implemented using the ComfyUI platform, chosen for its flexibility in customizing diffusion pipelines. We utilized the Kaggle dataset to train and evaluate our model. Our trained UNET diffusion model achieved reconstruction with a minimal loss of 0.307 after 800 optimization steps and an average stable Structural Similarity Index Measure (SSIM) of 0.69. This work represents a pre-processing step towards improving colposcopy image quality, thus enhancing the accuracy of cervical cancer diagnosis.
Colposcopy, Specular reflection, Unet Diffusion
Recognition of Objects Using Fast RCCN- Hybrid Particle Swarm Firefly Algorithm
-In the process of image analysis, the recognition and detection of objects is very important in big data analysis. In this paper, a fast and accurate detection of object is designed by utilizing faster recurrent neural network (F-RCNN) and hybrid optimization. Initially, the input dataset is processed and perform segmentation operation using K-Mean clustering. The FRCNN model helps in extracting the features of the image. The detailed information of pixels in the image will be retained using FRCNN. To improve the process of neural network, a hybrid optimization technique i.e., particle swarm optimization and firefly optimization (PSFA) is combined. The model is designed by integrating FRCNN and PSFA which improves the detection process in big data analysis. In this paper, the proposed model is evaluated using PASCAL VOC 2007 dataset. Objects like car, ship, cat, dog, horse, person etc are focussed and recognition of the objects in images are done from different class of objects. The integration of optimization with deep learning method gives a promising improvement in obtaining the objects of various classes. The mean average precision in detection of objects is evaluated using matlab software tool. The overall average precision rate achieved is 84.6% which is higher compared to other techniques
K-Mean clustering, Deep Learning, Convolutional Neural Network, Fast Recurrent CNN, Particle swarm optimization, Firefly algorithm
Enhancing Smart Wheelchair Navigation through Head Motion Detection: A YOLO-based Approach
In 2020, it was recorded that 5 percent of the total population in Indonesia were people with disabilities. People with physical disabilities, especially those who cannot function both their hands and feet are facing problem in their mobility. When most wheelchairs are controlled using hands, they will not be able to control the wheelchair by themselves. This research aims to create smart wheelchairs that use object detection models, to enable the user to navigate their wheelchair. The smart wheelchair is equipped with a camera that will capture the user's head movement and will move based on it. Deep learning model algorithms are used to detect the head movement. In this research, each variation of three generations of YOLO (You Only Look Once), YOLOv5, VOLOv6, and YOLOv7, are compared to find the proper one for the system. It is found that YOLOv6N has the fastest inference time, that is 2.54 ms. All the models are also evaluated on several parameters: Precision, recall, mAP@.5, and mAP@.5:.95. There’s no huge difference between each variation. All of the precision, recall and mAP@.5 of each variation are above 0.9. Yet, the difference can be seen for the mAP@.5:.95 where the highest score is 0.808 from YOLOv6L and the lowest is 0.703 from YOLOv5N.
Achieving High-End Image Localization via Causality Infused Renet50 model
The work involved a methodical approach to develop and assess a machine learning model for image localization. We have adapted a pre-trained ResNet50 model integrated with causality to develop high quality predictions for image localization. The model at its core utilizes optimized features that have been deduced using advanced algorithms. The overall proposed model has been developed and tested using the ImageNet dataset. We use a fault-proof mechanism in the refined dataset by assessing the relevance of causal relationships through Granger Causality. The use of principal component analysis for the development of the features used for the training process of our model is what sets it apart. We have been able to achieve high accuracy in our model, along with a validation accuracy of 99.7% in our model.
DAB-UNET: Dual Attention Block UNET Segmentation for Diabetic Retinopathy Utilizing an Encoder-Decoder Residual
Fundus images play an essential role in ophthalmic diagnostics for the detection of many eye illnesses. The experiment begins with a thorough image preprocessing technique, which includes clipping the circular borders, scaling the image, enhancing the contrast, removing noise, and augmenting the data. The new combined block applies to extracting distinctive deep feature representations, which help to detect the first shape of the edges of each lesion. It is namely the Attention Block and the Conv-Deconv UNET model. Attention Block is subsequently implemented in order to augment the robustness and quality of feature depictions derived from a pair of DR images. The Dual Attention Block for the backbone, which is supplemented with hierarchical bottleneck attention, is what we propose here referred to as DAB-UNET. In order to emphasize retinal anomalies that are significant for fovea macula and DR semantic segmentation in the deteriorated retina, the network is made up of a unique bottleneck attention block. We trained Mask-RCNN model that comprises of a backbone for eliminate OD regions. Moreover, the proposed block combines self-attention with channel attention in order to highlight these abnormalities. Our results indicate that DAB-UNET is potentially very effective for identifying landmarks even when dealing with different types of retinal degenerative disorders.
DBA-UNET, UNET Network, ResNet Model, Diabetic Retinopathy, Attention Block.
Enhancing Pneumonia Classification Performance through CNN Architecture Optimization and Hyperparameter Tuning
In the era of health digitalization, early detection of pneumonia through medical image analysis is one of the main challenges in improving the quality of health services. This study aims to enhance pneumonia classification performance using Convolutional Neural Network (CNN) architecture optimization and careful hyperparameter tuning. Through the application of optimization techniques such as Random Search, Bayesian Optimization, and tuning key hyperparameters such as the number of convolution layers, kernel size, dropout rate, and learning rate, this research succeeded in identifying the optimal model configuration. The Proposed Method model shows the best overall performance based on research results involving three models: Proposed Method, VGG16, and ResNet50. With the highest F1 Score value of 0.8440, accuracy of 0.9000, and lowest loss of 0.0977, the Proposed Method achieved an optimal balance between recall and Precision. Although VGG16 has the highest recall, its low precision value shows a tendency to produce more false positives. In contrast, the Proposed Method, with the best Precision of 0.7600 and superior accuracy performance, makes it the most reliable model for classifying pneumonia in this study. Experimental results show a significant increase in classification accuracy compared with conventional approaches, thus supporting further implementation in clinical applications. This study also provides insight into the importance of a systematic approach in designing and optimizing CNN models for disease classification tasks, especially pneumonia.
Pneumonia Classification, Convolutional Neural Network (CNN), Hyperparameter Tuning, CNN Architecture Optimization, Medical Imaging, Deep Learning, Image Classification