Contrast Limited Adaptive Histogram Equalization Research Articles

Multimodal biometric identification: leveraging convolutional neural network (CNN) architectures and fusion techniques with fingerprint and finger vein data

Advancements in multimodal biometrics, which integrate multiple biometric traits, promise to enhance the accuracy and robustness of identification systems. This study focuses on improving multimodal biometric identification by using fingerprint and finger vein images as the primary traits. We utilized the “NUPT-FPV” dataset, which contains a substantial number of finger vein and fingerprint images, which significantly aided our research. Convolutional neural networks (CNNs), renowned for their efficacy in computer vision tasks, are used in our model to extract distinct discriminative features. Specifically, we incorporate three popular CNN architectures: ResNet, VGGNet, and DenseNet. We explore three fusion strategies used in security applications: early fusion, late fusion, and score-level fusion. Early fusion integrates raw images at the input layer of a single CNN, combining information at the initial stages. Late fusion, in contrast, merges features after individual learning from each CNN model. Score-level fusion employs weighted aggregation to combine scores from each modality, leveraging the complementary information they provide. We also use contrast limited adaptive histogram equalization (CLAHE) to enhance fingerprint contrast and vein pattern features, improving feature visibility and extraction. Our evaluation metrics include accuracy, equal error rate (EER), and ROC curves. The fusion of CNN architectures and enhancement methods shows promising performance in identifying multimodal biometrics, aiming to increase identification accuracy. The proposed model offers a reliable authentication system using multiple biometrics to verify identity.

MRI Image Enhancement Using Multilevel Image Thresholds Based on Contrast-limited Adaptive Histogram Equalization

Magnetic resonance imaging (MRI) creates detailed images by combining a powerful magnetic field with high-frequency radio waves. MRI images need to increase brightness and contrast to facilitate distinguishing diseases. This study aims to improve the MRI images depending on the suggested algorithm, including three main stages. The first is to divide the image into three main areas using Multilevel image thresholds and then improve using CLAHE for each area separately. Finally, these images combine to form one image and improve it again. By analyzing the results and calculating non-referenced quality measures, the proposed method obtained the best quality results compared to the rest of the methods with quality rates of EN(6.14), AG(6.79), and CEM(0.84), which indicates excellent success in increasing clarity in those images.

High-fidelity Image Restoration of Large 3D Electron Microscopy Volume.

Volume electron microscopy (VEM) is an essential tool for studying biological structures. Due to the challenges of sample preparation and continuous volumetric imaging, image artifacts are almost inevitable. Such image artifacts complicate further processing both for automated computer vision methods and human experts. Unfortunately, the widely used contrast limited adaptive histogram equalization (CLAHE) can alter the essential relative contrast information about some biological structures. We developed an image-processing pipeline to remove the artifacts and enhance the images without CLAHE. We apply our method to VEM datasets of a Microwasp head. We demonstrate that our method restores the images with high fidelity while preserving the original relative contrast. This pipeline is adaptable to other VEM datasets.

DFC-Net: a dual-path frequency-domain cross-attention fusion network for retinal image quality assessment

Retinal image quality assessment (RIQA) is crucial for diagnosing various eye diseases and ensuring the accuracy of diagnostic analyses based on retinal fundus images. Traditional deep convolutional neural networks (CNNs) for RIQA face challenges such as over-reliance on RGB image brightness and difficulty in differentiating closely ranked image quality categories. To address these issues, we introduced the Dual-Path Frequency-domain Cross-attention Network (DFC-Net), which integrates RGB images and contrast-enhanced images using contrast-limited adaptive histogram equalization (CLAHE) as dual inputs. This approach improves structure detail detection and feature extraction. We also incorporated a frequency-domain attention mechanism (FDAM) to focus selectively on frequency components indicative of quality degradations and a cross-attention mechanism (CAM) to optimize the integration of dual inputs. Our experiments on the EyeQ and RIQA-RFMiD datasets demonstrated significant improvements, achieving a precision of 0.8895, recall of 0.8923, F1-score of 0.8909, and a Kappa score of 0.9191 on the EyeQ dataset. On the RIQA-RFMiD dataset, the precision was 0.702, recall 0.6729, F1-score 0.6869, and Kappa score 0.7210, outperforming current state-of-the-art approaches.

Early-stage cardiomegaly detection and classification from X-ray images using convolutional neural networks and transfer learning

Cardiomyopathy is a serious condition that can result in heart failure, sudden cardiac death, malignant arrhythmias, and thromboembolism. It is a significant contributor to morbidity and mortality globally. The initial finding of cardiomegaly on radiological imaging may signal a deterioration of a known heart condition, an unknown heart disease, or a heart complication related to another illness. Further cardiological evaluation is needed to confirm the diagnosis and determine appropriate treatment. A chest radiograph (X-ray) is the main imaging method used to identify cardiomegaly when the heart is enlarged. A prompt and accurate diagnosis is essential to help healthcare providers determine the most appropriate treatment options before the condition worsens. This study aims to utilize convolutional neural networks and transfer learning techniques, specifically Inception, DenseNet-169, and ResNet-50, to classify cardiomegaly from chest X-ray images automatically. The utilization of block-matching and 3D filtering (BM3D) techniques aimed at enhancing image edge retention, decreasing noise, and utilizing contrast limited adaptive histogram equalization (CLAHE) to enhance contrast in low-intensity images. Gradient-weighted Class Activation Mapping (GradCAM) was used to visualize the significant activation regions contributing to the model's decision. After evaluating all the models, the ResNet-50 model showed outstanding performance. It achieved perfect accuracy of 100 % in both training, and validation, and an impressive 99.8 % accuracy in testing. Additionally, it displayed complete 100 % precision, recall, and F1-score. These findings demonstrate that ResNet-50 surpassed all other models in the study. As a result, the impressive performance of the ResNet-50 model suggests that it could be a valuable tool in improving the efficiency and accuracy of cardiomyopathy diagnosis, ultimately leading to better patient outcomes.

Performance Comparison of GLCM Features and Preprocessing Effect on Batik Image Retrieval

The use of the Grey-Level Co-occurrence Matrix (GLCM) for feature extraction in image retrieval with complex motifs, such as batik images, has been widely used. Some features often extracted include energy, entropy, correlation, and contrast. Other than these four features, the addition of dissimilarity and homogeneity features to the GLCM method is proposed in this study. Preprocessing methods such as Histogram Equalization (HE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) are also used to see whether the two methods can increase the precision value of the retrieval results. This study used the Batik 300 dataset, which consists of 50 classes. Batik was chosen because this type of image has complex patterns and motifs so that it will maximize the role of the GLCM method itself. In addition, Batik is also a world heritage art, so its sustainability needs to be maintained. The test results show that adding dissimilarity and homogeneity features and using the CLAHE method in the preprocessing step can improve model performance. Combining these two methods has produced higher precision values than not using either. Batik, a globally recognized art form, holds the status of a world heritage, necessitating the preservation of its sustainability. Test results have demonstrated that incorporating dissimilarity and homogeneity features, alongside using the CLAHE method during the preprocessing stage, leads to enhanced model performance. The amalgamation of these two methods has yielded precision values that surpass those achieved when either method is used in isolation.

Deep Learning-Based Classification of Fruit Diseases - A Comprehensive Approach to Automated Detection and Diagnosis

This study aims to develop a deep learning-based model for accurate fruit disease classification using advanced architectures like ResNet50 and VGG19. The dataset includes images of fruits such as grapes, mangoes, lemons, pomegranates, and guavas, with both healthy and diseased categories. Diseases like black rot in grapes and bacterial canker in mangoes are considered for classification. The objective is to build a model capable of accurately identifying these diseases to aid in early identification and control. The approach begins with pre-processing the photographs using standardisation, resizing, and data augmentation techniques including flipping, zooming, and rotation to strengthen the model. By enhancing picture contrast with Contrast Limited Adaptive Histogram Equalisation (CLAHE), important illness characteristics can be better highlighted. We train and evaluate deep learning models by dividing the dataset into three parts: training, validation, and test. The ratio of the parts is 80:10:10. According on the results, the ResNet50 model outperforms all other accuracy at 99.77%.

Hybrid ensemble deep learning model for advancing breast cancer detection and classification in clinical applications

Being the most common type of cancer worldwide, and affecting over 2.3 million women, breast cancer poses a significant health threat. Although survival rates have improved around the world due to advances in screening, diagnosis, and treatment, early detection remains crucial for effective management. This study seeks to introduce a novel hybrid model that makes use of image-preprocessing techniques and deep-learning algorithms on mammograms to enhance the detection and classification accuracy of breast cancer lesions. The model was tested on a dataset comprising 20,000 mammograms. First, image-processing techniques, such as Contrast-Limited Adaptive Histogram Equalization, Gaussian Blur, and sharpening methods were used to optimize the images for enhanced feature extraction. In addition, the Ensemble Deep Random Vector-Functional Link Neural Network algorithm, YOLOv5, and MedSAM segmentation models were utilized for robust deep learning-based extraction, classification, and visualization of lesions. Finally, the model was clinically validated on 800 patients. The study found a notable enhancement in both accuracy and processing time for benign and malignant diagnoses using the hybrid model. The model achieves an impressive accuracy of 99.7 % and demonstrates a remarkable processing time of 0.75 s. In clinical applications, the hybrid model exhibits high proficiency, reporting 97.2 % accuracy for benign cases and 98.6 % for malignant scenarios. These results highlight the effectiveness of the hybrid model in improving diagnostic accuracy, offering a promising tool for early breast cancer detection.

Artificial Intelligence Tools in Pediatric Urology: A Comprehensive Review of Recent Advances.

Artificial intelligence (AI) is providing novel answers to long-standing clinical problems, and it is quickly changing pediatric urology. This thorough analysis focuses on current developments in AI technologies that improve pediatric urology diagnosis, treatment planning, and surgery results. Deep learning algorithms help detect problems with previously unheard-of precision in disorders including hydronephrosis, pyeloplasty, and vesicoureteral reflux, where AI-powered prediction models have demonstrated promising outcomes in boosting diagnostic accuracy. AI-enhanced image processing methods have significantly improved the quality and interpretation of medical images. Examples of these methods are deep-learning-based segmentation and contrast limited adaptive histogram equalization (CLAHE). These methods guarantee higher precision in the identification and classification of pediatric urological disorders, and AI-driven ground truth construction approaches aid in the standardization of and improvement in training data, resulting in more resilient and consistent segmentation models. AI is being used for surgical support as well. AI-assisted navigation devices help with difficult operations like pyeloplasty by decreasing complications and increasing surgical accuracy. AI also helps with long-term patient monitoring, predictive analytics, and customized treatment strategies, all of which improve results for younger patients. However, there are practical, ethical, and legal issues with AI integration in pediatric urology that need to be carefully navigated. To close knowledge gaps, more investigation is required, especially in the areas of AI-driven surgical methods and standardized ground truth datasets for pediatric radiologic image segmentation. In the end, AI has the potential to completely transform pediatric urology by enhancing patient care, increasing the effectiveness of treatments, and spurring more advancements in this exciting area.

Lung cancer classification model using convolutional neural network with feature ranking process

Abstract Lung cancer is the leading cause of cancer-related deaths worldwide, highlighting the importance of early detection to improve patient outcomes. The goal of this study is to create a computer-aided diagnosis (CAD) system that detects and classifies lung cancer based on medical images using a Convolutional Neural Network (CNN) and feature extraction techniques. The study employs the LIDC-IDRI dataset, a comprehensive collection of lung cancer-related medical images, to achieve this goal. Contrast Limited Adaptive Histogram Equalization (CLAHE) is used to improve contrast and detail while reducing noise. The Sobel operator is used to refine the segmentation process by enhancing edges and contours in binary images. Morphological operations such as dilation and filling are used to refine the segmented regions even further. Feature extraction is used to extract statistical data and texture characteristics from segmented regions. Mean and variance calculations reveal information about brightness and variability within regions, whereas co-occurrence matrices and Gray-Level Co-occurrence Matrix (GLCM) properties quantify texture features. The correlation between different regions is also evaluated to assess their relationships. Using the pre-processed and ranked features as inputs, a CNN model with five hidden layers is trained. To classify the segmented regions into cancerous and non-cancerous classes, the model learns patterns and relationships in the data. A confusion matrix is used to assess the accuracy, specificity, and sensitivity of the model's predictions, with an emphasis on correctly identifying lung cancer-affected regions. The results show promising results, with the proposed CAD system identifying lung cancer-affected regions with an accuracy of 99.4375%. The system also outperforms other existing methods with a specificity of 99.12% and a sensitivity of 99.26%. These findings highlight the developed system's potential as a valuable tool for early lung cancer detection, assisting doctors in making accurate diagnoses and improving patient outcomes.

Enhanced diagnostic accuracy for multiple lung diseases using a fine-tuned MobileNetV2 model with advanced pre-processing techniques

Early diagnosis of lung conditions is crucial for effective treatment and improving patient health. However, traditional diagnostic methods using chest X-ray images have some notable drawbacks, such as overlapping anatomical structures obscuring areas of interest, the presence of noise potentially masking abnormalities, and low contrast diminishing differentiation. In this research undertaking, we explored an enhanced MobileNetV2 approach to augmenting the accuracy of diagnosing multiple concomitant lung pathologies. We employed an inclusive methodology, incorporating several progressive steps. We leveraged contrast-limited adaptive histogram equalization to heighten the clarity of the dataset’s images. Bilateral filtering was applied to refine contrast and sharpness, along with utilizing a dense convolutional neural network. Additional techniques were utilized as well, such as image standardization, Gaussian blur, and histogram equalization, to further increase contrast and reduce noise. This was performed through rotations, scaling, horizontal flipping, brightness adjustment, elastic transformations, and random cropping and padding to generate more realistic exemplars. We defined and compiled a purpose-specific MobileNetV2 architecture for this research endeavor. The model was evaluated based on several metrics, including accuracy, precision, recall, F1-score, and specificity, after every epoch, to refine the training process. We used Bayesian optimization for more efficient fine-tuning of the model. Conditional random fields and ensemble averaging methods were employed for postprocessing. These enhancements led to superior results. After image enhancement, the model achieved an accuracy of 99.20 %, with a precision of 98.90 %, a recall of 99.10 %, and an F1-score of 99.00 %. The specificity of the predictions reached 99.40 %. These improvements assist medical workers in making more informed decisions. However, it is important to note that the current algorithm does not influence the effectiveness of treatment or recovery from the condition.

Deep Feature Extraction with SVM Classifier for Brain Tumor Classification

Recently, methods for classifying brain tumors have used Deep Learning (DL), in particular, Convolutional Neural Networks (CNNs), to obtain promising results. Unlike conventional Machine Learning (ML) algorithms, CNNs can automatically extract features from images; they do not, however, need the extraction regarding hand-crafted features. Many obstacles reduce the accuracy of brain tumor classification systems; to address such obstacles, we propose a DL-based feature extraction method for brain tumor classification. A total of two pretrained CNNs, VggNet-16 and ResNet-18, are applied as deep feature extractors in the scheme to accomplish deep feature extraction. Contrast Limited Adaptive Histogram Equalization (CLAHE) technique has been first used for enhancing the images. Second, each separately pretrained CNN, VggNet-16 and ResNet-18, is used for extracting the deep features from Magnetic Resonance Imaging (MRI). Lastly, a Multiclass Support Vector Machines (Multiclass -SVM) classifier is used to complete a classification task. Tests conducted on the aforementioned datasets: the effectiveness of the suggested approach is demonstrated by the CE-MRI Figshare, the REMBRANDT, and the Brain Tumor Classification, where the pre-trained CNN models and SVM classifier enhance performance. Specifically, the pretrained CNN with SVM approach produces accuracy ranging from 95.28% to 98.62% across all data-sets.

Classification and segmentation of kidney MRI images for chronic kidney disease detection

Chronic Kidney Disease (CKD) is a common ailment with significant public health implications, underscoring the critical importance of early detection and diagnosis for effective management. Our research focuses on utilizing deep learning models to accurately classify and segment kidney MRI images, aiding in the timely detection and diagnosis of CKD. Utilizing the Zenodo dataset comprising 1299 kidney MRI images from 100 T2-weighted abdomen MRI scans, our investigation reveals that Vision Transformers (ViT) based model EfficientNet_b1 exhibited superior performance compared to other models. It achieved an accuracy of 94.38% in distinguishing between healthy and CKD cases in kidney MRI image classification. Moreover, the pretrained models Densenet201 and VGG19, as well as the ViT-based models EfficientNet_b1 and tf_mobilenetv3_large_075, demonstrate remarkable sensitivity and accuracy while compared to identifying kidney MRI images which highlights the effectiveness of deep learning architectures in identifying diseases. Additionally, our segmentation analysis reveals that our proposed network, Resnet18-Self-ONN-UNet++, adeptly delineates kidney structures. The network shows remarkable performance in medical diagnosis and treatment planning, with an Intersection over Union (IoU) of 82.34% and DS of 91.57% when integrated with the Contrast Limited Adaptive Histogram Equalization (CLAHE) preprocessing technique and Simultaneous truth and performance level estimation (STAPLE) as post processing technique. In summary, deep learning-driven kidney MRI image classification and segmentation enhance medical imaging analysis, potentially aiding in early CKD diagnosis and treatment, thereby addressing critical public health concerns, and enhancing patient outcomes.

GSAAN with War Strategy Optimization Algorithm for Cyber Security in a 5G Wireless Communication Network

In this paper, the graph sample and aggregate-attention network with war strategy optimization algorithm for cyber security in the 5G wireless communication network (CS-5GWCN-GSAAN-WSOA) is proposed in 5G mobile networks to identify cyber threats. Initially, the input data are amassed from the 5G-NIDD dataset. Then the input data are fed to preprocessing, here redundancy elimination and missing value replacement are performed utilizing the contrast limited adaptive histogram equalization filtering (CLAHEF) method. After preprocessing, optimal features are selected by the stochastic gradient boosting based recursive feature elimination process. The selected features are fed to the graph sample and aggregate-attention network (GSAAN) to detect cyber-attacks in 5G wireless communication. Generally, the GSAAN approach does not adopt any optimization techniques for determining optimal parameters and guaranteeing accurate attack detection. Therefore, the war strategy optimization algorithm (WSOA) contemplates to optimize the GSAAN weight parameters. The CS-5GWCN-GSAAN-WSOA method is activated on Python. The CS-5GWCN-GSAAN-WSOA method attains 22.51%, 20.35%, and 35.54% higher accuracy, 19.45%, 27.80%, and 18.93% higher sensitivity analysed with existing models, like cyber security attack identification in 5G wireless systems utilizing machine learning (CS-5GWCN-SVM), enhanced dropping attacks detection in 5G networks depending on deep learning models (CS-5GWCN-KNN), and cyber security issues in 5G enabled attack detection through deep learning (CS-5GWCN-RNN), respectively.

Graph-based adaptive weighted fusion SLAM using multimodal data in complex underground spaces

Accurate and robust simultaneous localization and mapping (SLAM) is essential for autonomous exploration, unmanned transportation, and emergency rescue operations in complex underground spaces. However, the demanding conditions of underground spaces, characterized by poor lighting, weak textures, and high dust levels, pose substantial challenges to SLAM. To address this issue, we propose a graph-based adaptive weighted fusion SLAM (AWF-SLAM) for autonomous robots to achieve accurate and robust SLAM in complex underground spaces. First, a contrast limited adaptive histogram equalization (CLAHE) that combined adaptive gamma correction with weighting distribution (AGCWD) in hue, saturation, and value (HSV) space is proposed to enhance the brightness and contrast of visual images in underground spaces. Then, the performance of each sensor is evaluated using a consistency check based on the Mahalanobis distance to select the optimal configuration for specific conditions. Subsequently, we elaborate an adaptive weighting function model, which leverages the residuals from point cloud matching and the inner point rate of image matching. This model fuses data from light detection and ranging (LiDAR), inertial measurement unit (IMU), and cameras dynamically, enhancing the flexibility of the fusion process. Finally, multiple primitive features are adaptively fused within the factor graph optimization, utilizing a sliding window approach. Extensive experiments were conducted to check the performance of AWF-SLAM using a self-designed mobile robot in underground parking lots, excavated subway tunnels, and complex underground coal mine spaces based on reference trajectories and reconstructions provided by state-of-the-art methods. Satisfactorily, the root mean square error (RMSE) of trajectory translation is only 0.17 m, and the mean relative robustness distance between the point cloud maps reconstructed by AWF-SLAM and the reference point cloud map is lower than 0.09 m. These results indicate a substantial improvement in the accuracy and robustness of SLAM in complex underground spaces.

Medical X-Ray Image Enhancement Using Global Contrast-Limited Adaptive Histogram Equalization

In medical imaging, accurate diagnosis heavily relies on effective image enhancement techniques, particularly for X-ray images. Existing methods often suffer from various challenges such as sacrificing global image characteristics over local image characteristics or vice versa. In this paper, we present a novel approach, called G-CLAHE (Global-Contrast Limited Adaptive Histogram Equalization), which perfectly suits medical imaging with a focus on X-rays. This method adapts from Global Histogram Equalization (GHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) to take both advantages and avoid weakness to preserve local and global characteristics. Experimental results show that it can significantly improve current state-of-the-art algorithms to effectively address their limitations and enhance the contrast and quality of X-ray images for diagnostic accuracy.

Allergy Wheal and Erythema Segmentation Using Attention U-Net.

The skin prick test (SPT) is a key tool for identifying sensitized allergens associated with immunoglobulin E-mediated allergic diseases such as asthma, allergic rhinitis, atopic dermatitis, urticaria, angioedema, and anaphylaxis. However, the SPT is labor-intensive and time-consuming due to the necessity of measuring the sizes of the erythema and wheals induced by allergens on the skin. In this study, we used an image preprocessing method and a deep learning model to segment wheals and erythema in SPT images captured by a smartphone camera. Subsequently, we assessed the deep learning model's performance by comparing the results with ground-truth data. Using contrast-limited adaptive histogram equalization (CLAHE), an image preprocessing technique designed to enhance image contrast, we augmented the chromatic contrast in 46 SPT images from 33 participants. We established a deep learning model for wheal and erythema segmentation using 144 and 150 training datasets, respectively. The wheal segmentation model achieved an accuracy of 0.9985, a sensitivity of 0.5621, a specificity of 0.9995, and a Dice similarity coefficient of 0.7079, whereas the erythema segmentation model achieved an accuracy of 0.9660, a sensitivity of 0.5787, a specificity of 0.97977, and a Dice similarity coefficient of 0.6636. The use of image preprocessing and deep learning technology in SPT is expected to have a significant positive impact on medical practice by ensuring the accurate segmentation of wheals and erythema, producing consistent evaluation results, and simplifying diagnostic processes.

Iridology based human health conditions predictions with computer vision and deep learning

In today’s world, ocular diseases have become widespread. The “ocular illness” refers to any abnormality, impairment, or dysfunction of vision that impacts the eye. The most common forms of ocular disease include dry eyes, conjunctivitis, blepharitis, and glaucoma. Each of these conditions can cause redness, pain, and blurry vision. To resolve the challenges in human ocular diseases, the need for detection becomes an important part of every person’s life. There are numerous approaches by various researchers to detect and predict the disease early, but the previous approaches required certain improvements to predict the ocular disease more accurately and easily. In the proposed method, deep learning-based ocular disease prediction with high accuracy and low cost is implemented. The framework deals with the collection of data from the diabetes-iridology dataset and pre-processing using the Contrast limited adaptive histogram equalization (CLAHE). The Attenuation-based U-net is the most advanced proposed method used for the segmentation of ocular disease with higher accuracy. The Adaptive Residual Squeeze and Excitation network (ARSEnet) is the proposed method used for a better classification process. Based on the evaluation results, the proposed technique better classified eye illness. The Python tool is used to carry out the recommended method. The proposed strategy outperformed others in terms of accuracy, precision, specificity, recall, and f1 score. The accuracy rate of the proposed method is 97.02%. The resultant analysis clearly proves that the proposed system provides the best accuracy for predicting ocular disease.

Potential Screening, Grading and Follow-Up of Diabetic Retinopathy in Primary Care Using Artificial Intelligence – How Hard Would It Be to Implement? An Ophthalmologist’s Perspective

Diabetic retinopathy (DR) is a microvascular disorder caused by the long-term effects of diabetes mellitus and among the primary causes of blindness worldwide. Early detection of DR is the key to its effective treatment and subsequent reduction of associated economic burden, but manual screening is time-consuming and of limited availability. A highly sensitive and specific automatic diagnostic tool would significantly improve screening programs and allow referring for further evaluation and treatment in an ophthalmology clinic only patients with significant lesions or with changes between two successive evaluations. Several deep learning-based automated diagnosis tools have been proposed to aid screening but their implementation with minimal costs is not accessible to physicians with no coding knowledge. We aimed to develop a fundus images classification model with no coding knowledge by using generative artificial intelligence (AI) implemented in Windows 11 operating system under subscription (Copilot Pro), a free image analysis tool (Fiji ImageJ2), and Vertex AI, a machine learning (ML) platform launched by Google in 2021. For this purpose, we selected a total of 2961 labelled cases from the APTOS 2019 database of DR fundus images. Images were batch segmented using a Java ImageJ script generated by Copilot Pro and based on the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm. Segmented images were used to train an automated ML classification model to detect DR severity (5 classes – no DR, mild non-proliferative DR, moderate DR, severe DR, proliferative DR). The model achieved an area under the precision-recall curve of 0.889, with a precision rate of 83.8% and a recall rate of 77%. In conclusion, generative AI implemented into Windows operating system together with a free imaging processing tool and Vertex AI allow ophthalmologists with no coding knowledge to benefit from publicly available image databases (thousands of cases) to develop accurate automated diagnostic tools. Such tools have the potential to facilitate screening especially in areas with few specialists.

Hybrid Ensemble Deep Learning Model for Advancing Ischemic Brain Stroke Detection and Classification in Clinical Application.

Ischemic brain strokes are severe medical conditions that occur due to blockages in the brain's blood flow, often caused by blood clots or artery blockages. Early detection is crucial for effective treatment. This study aims to improve the detection and classification of ischemic brain strokes in clinical settings by introducing a new approach that integrates the stroke precision enhancement, ensemble deep learning, and intelligent lesion detection and segmentation models. The proposed hybrid model was trained and tested using a dataset of 10,000 computed tomography scans. A 25-fold cross-validation technique was employed, while the model's performance was evaluated using accuracy, precision, recall, and F1 score. The findings indicate significant improvements in accuracy for different stages of stroke images when enhanced using the SPEM model with contrast-limited adaptive histogram equalization set to 4. Specifically, accuracy showed significant improvement (from 0.876 to 0.933) for hyper-acute stroke images; from 0.881 to 0.948 for acute stroke images, from 0.927 to 0.974 for sub-acute stroke images, and from 0.928 to 0.982 for chronic stroke images. Thus, the study shows significant promise for the detection and classification of ischemic brain strokes. Further research is needed to validate its performance on larger datasets and enhance its integration into clinical settings.