Retinal fundus imaging-based diabetic retinopathy classification using transfer learning and fennec fox optimization.

Automatic Detection and Classification of Diabetic Retinopathy from Retinal Fundus Images by Abdullah Biran, Master of Applied Science, lectrical and computer engineering Department, Ryerson University, 2017. Diabetic Retinopathy (DR) is an eye disease that leads to blindness when it progresses to proliferative level. The earliest signs of DR are the appearance of red and yellow lesions on the retina called hemorrhages and exudates. Early diagnosis of DR prevents from blindness. In this thesis, an automatic algorithm for detecting diabetic retinopathy is presented. The algorithm is based on combination of several image processing techniques including Circular Hough Transform (CHT), Contrast Limited Adaptive Histogram Equalization (CLAHE), Gabor filter and thresholding. In addition, Support Vector Machine (SVM) classifier is used to classify retinal images into normal or abnormal cases of DR including non-proliferative (NPDR) or proliferative diabetic retinopathy (PDR). The proposed method has been tested on fundus images from Standard Diabetic Retinopathy Database (DIARETDB). The implementation of the presented methodology was done in MATLAB. The methodology is tested for sensitivity and accuracy.

Diabetic Retinopathy (DR) has become a widespread illness among diabetics across the globe. Retinal fundus images are generally used by physicians to detect and classify the stages of DR. Since manual examination of DR images is a time-consuming process with the risks of biased results, automated tools using Artificial Intelligence (AI) to diagnose the disease have become essential. In this view, the current study develops an Optimal Deep Learning-enabled Fusion-based Diabetic Retinopathy Detection and Classification (ODL-FDRDC) technique. The intention of the proposed ODL-FDRDC technique is to identify DR and categorize its different grades using retinal fundus images. In addition, ODL-FDRDC technique involves region growing segmentation technique to determine the infected regions. Moreover, the fusion of two DL models namely, CapsNet and MobileNet is used for feature extraction. Further, the hyperparameter tuning of these models is also performed via Coyote Optimization Algorithm (COA). Gated Recurrent Unit (GRU) is also utilized to identify DR. The experimental results of the analysis, accomplished by ODL-FDRDC technique against benchmark DR dataset, established the supremacy of the technique over existing methodologies under different measures.

Chapter 3 - Classification of Diabetic Retinopathy

Diabetic Retinopathy (DR) is a progressive eye disease that can lead to vision loss and blindness if left untreated. Ophthalmologists diagnose DR using medical imaging modalities such as fundus photography and optical coherence tomography (OCT); however, manual interpretation of these images is time-consuming and subject to inter-observer variability. While DR is irreversible, vision loss can be prevented through early detection and timely intervention. Regular eye examinations and systematic DR monitoring are essential for preventing blindness in diabetic patients. Therefore, there is an urgent need for computer-assisted diagnosis (CAD) systems to support ophthalmologists in detecting and grading DR accurately and efficiently. This paper proposes a novel CAD system for automated DR classification. The proposed methodology consists of three stages: (1) image preprocessing through grayscale conversion and resizing, (2) retinal vessel segmentation using a local thresholding approach, and (3) classification using a hybrid architecture that integrates a Convolutional Neural Network (CNN) with a Support Vector Machine (SVM) classifier. The proposed model, OptiCNN (Optimized CNN), aligns its predictions with the International Clinical Diabetic Retinopathy (ICDR) severity scale. Experimental results demonstrate that OptiCNN achieves an accuracy of 94%, precision of 96%, recall of 91%, F1-score of 93%, and area under the curve (AUC) of 92% on benchmark datasets. The proposed system provides reliable DR staging to assist ophthalmologists in treatment planning and clinical decision-making, potentially reducing diagnostic workload while maintaining high diagnostic accuracy.

Diabetic retinopathy (DR) is a dangerous eye condition that affects diabetic patients. Without early detection, it can affect the retina and may eventually cause permanent blindness. The early diagnosis of DR is crucial for its treatment. However, the diagnosis of DR is a very difficult process that requires an experienced ophthalmologist. A breakthrough in the field of artificial intelligence called deep learning can help in giving the ophthalmologist a second opinion regarding the classification of the DR by using an autonomous classifier. To accurately train a deep learning model to classify DR, an enormous number of images is required, and this is an important limitation in the DR domain. Transfer learning is a technique that can help in overcoming the scarcity of images. The main idea that is exploited by transfer learning is that a deep learning architecture, previously trained on non-medical images, can be fine-tuned to suit the DR dataset. This paper reviews research papers that focus on DR classification by using transfer learning to present the best existing methods to address this problem. This review can help future researchers to find out existing transfer learning methods to address the DR classification task and to show their differences in terms of performance.

Diabetic retinopathy, a retinal disorder resulting from diabetes mellitus, is a prominent cause of visual degradation and loss among the global population. Therefore, the identification and classification of diabetic retinopathy are of utmost importance in the clinical diagnosis and therapy. Currently, these duties are extensively carried out by manual examination utilizing the human visual system. Nevertheless, manual examination is sometimes arduous, time-consuming, and prone to errors. Deep learning-based methods have recently demonstrated encouraging results in several areas, such as image categorization and natural language mining. The majority of deep learning techniques developed for medical image analysis rely on convolutional modules to extract the inherent structure of images within a certain local receptive field. Furthermore, transformer-based models have been utilized to tackle medical image processing problems by capitalizing on global connections among distant pixels in the images. Considering these analyses, this work presents a comprehensive deep learning model that combines convolutional neural network and vision mamba models. This model is designed to accurately identify and classify diabetic retinopathy lesions displayed in fundus images. Furthermore, the vision mamba component incorporates the bidirectional state space method and positional embedding to enable the location sensitivity of visual data samples and meet the conditions for global relationship context. An evaluation of the suggested method was carried out by comparison experiments between state-of-the-art algorithms and the proposed methodology. Empirical findings demonstrate that the suggested methodology surpasses the most advanced algorithms on the datasets that are accessible openly. Hence, the suggested approach may be regarded as a helpful tool for therapeutic processes.

Diabetic retinopathy (DR) is an eye disorder triggered by diabetes that might result in loss of sight. Earlier diagnosis of DR is critical since it might cause loss of sight. Manual diagnoses of DR severity by ophthalmologists are time-consuming and challenging. As a result, there has been considerable attention on designing an automatic technique for DR detection using fundus photographs. In medical science, prognosis and diagnosis are the most challenging tasks due to the presence of fuzziness in medical images and the restricted subjectivity of the experts. Neutrosophic Set (NS) in medical image analysis provides an understanding of the NS concepts, together with knowledge of how to collect, handle, interpret, and analyze clinical images using NS techniques. The neutrosophic set (NS), which is a generality of fuzzy set, provides the overcoming prospect of the restriction of fuzzy-based models for the analysis of medical images. This manuscript develops a Robust Diabetic Retinopathy Detection and Grading using Neutrosophic Topological Vector Space (DRDG-NSTVS) technique on fundus images. The DRDG-NSTVS technique begins with Median Filter (MF) noise removal to optimize the clarity of fundus photographs by successfully eliminating noises. Later, the InceptionV3 is used to perform feature extraction for identifying complicated features and patterns related to DR. The parameter tuning is performed by the moth flame optimization (MFO) technique to ensure superior performance of the model. The final diagnoses and classification of DR are accomplished utilizing the NSTVS classifiers that easily perform the uncertainties inherent in medicinal statistics. The simulation was conducted on a benchmark dataset to examine the proposed model performance. This combined method gives a greatly reliable and accurate solution for the earlier diagnosis and detection of DR

Diabetic retinopathy (DR) the prime cause of blindness, develops when glucose levels rise, causing retinal damage. DR can be prevented if the illness is detected early. As a result, early grading, categorization, and diagnosis of DR can help diabetic patients avoid visual loss. Several system methods assist in the classification of DR using high-performance criteria. This work proposes an efficient system-based DR classification. The purpose of efficient machine learning dabetic retinopatyy grading classification (EML-DRGC) design is to recognize DR impulsively with highest accuracy. The proposed technique employs preprocessing methods such as employing the Gaussian filtering approach for removing noise present in retinal fundus images. The segmentation process is followed using K-means segmentation algorithm which is used for segmenting the region of interest (ROI) from background. Moreover, Feature extraction process is done by using gray level co-occurrence matrix (GLCM) in which features are extracted bycapturing the image's visual content and features from acceerated segment test (FAST) design is used as extractor of features. Finally, multi support vector machine is utilized as classifier for detecting severity levels of DR. Performance metrics such as accuracy of 98.38% and specificity of 98.34% are obtained which are superior to existing designs.

Diabetic Retinopathy (DR) and Glaucoma are the ocular diseases that are main causes of vision loss. DR and glaucoma are asymptomatic in nature. Early detection can help to reduce disease-related vision loss. Manual method of detecting these diseases is time consuming, expensive and requires skilled supervision. Factors such as poor contrast of fundus images, disease-specific segmentation approach and anatomical factors may all lead to inaccuracies in segmentation-based approach for localizing landmarks. Automated diagnosis of DR and glaucoma can overcome the shortcomings of manual method and reduce the burden on ophthalmologists during mass screening. This work proposes an automated framework for detection of DR and glaucoma using non-linear features that include Higher Order Spectra (HOS), various types of entropies (Kapur entropy, Renyi entropy, Yager entropy and Shannon entropy) and Fractal Dimension (FD). These non-linear features are capable of capturing minute pixel variations in fundus images for classification of normal, DR or glaucoma. For classification, Support Vector Machine (SVM) classifier with different kernels was used. Results shows that SVM-Radial Basis Function (RBF) kernel combination resulted in maximum accuracy of 85%, sensitivity of 84% and specificity of 94.32%. Based on the results it shows that the three-class classification of normal, DR and glaucoma using non-linear feature analysis and SVM can be useful for mass screening.

Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: a clinical validation study

ABSTRACTThe early detection of diabetic retinopathy (DR) illnesses improves diagnosis and lowers the risk of permanent blindness. As a result, screening for DR in fundus images is an important method used to diagnose diabetes and other eye diseases. However, detecting diseases manually requires a significant amount of time and work. Deep learning (DL) techniques have produced encouraging results in categorizing fundus images. Still, the multi‐class DR disease remains a difficult task. Thus, the proposed framework adopted a novel Criss‐Cross Attention‐Based Squeeze‐and‐Excitation Assisted Vision Transformer (CCA‐SE‐ViT) model to classify DR from fundus images. Initially, the defective region of the retina is segmented using a novel dilated depth‐wise separable convolutional U‐Net model (dDSC‐UNet). Then, using the segmented regions, the fundus images are classified into multiple classes as age‐related macular degeneration (AMD), DR, glaucoma, cataracts, myopia, hypertension, normal, other abnormalities, and DR cases are classified into no DR, mild, moderate, severe, and proliferative DR, respectively. The retinal fundus images are obtained from publicly available datasets like OIA‐ODIR and APTOS 2019. The proposed methodology for multi‐class categorization of retinal illnesses in the OIA‐ODIR dataset yielded 97.2% accuracy, 96.7% precision, 96.1% recall, 95.9% F1‐score, and 96.4% specificity. The APTOS dataset was used for multi‐class classification of DR illnesses, and the results were 99.68% accuracy, 99.08% precision, 99.31% recall, 99.19% F1‐score, and 99.26% specificity. The results demonstrated that the proposed method accurately identifies DR using retinal fundus images.

Diabetic Retinopathy (DR) is one of the leading causes of preventable blindness worldwide, and early detection with precise severity classification is critical to reduce vision loss.Retinal fundus imaging is the most widely used modality for DR screening, but manual diagnosis is time-consuming, subjective, and prone to variability.Existing deep learning methods often suffer from noise sensitivity, overfitting, and limited ability to classify across all severity levels, which reduces their clinical applicability in large-scale screening.To address these limitations, this work is motivated by the need for an automated, accurate, and robust DR detection framework that can effectively support ophthalmologists in timely intervention.The novelty of this study lies in the development of a Fuzzy-Embedded Recurrent Neural Network (FERNN) combined with a Bilinear Double-Order Filter (BDOF).The BDOF enhances fundus image clarity by suppressing noise while preserving fine retinal details, and FERNN integrates fuzzy logic with recurrent learning to manage uncertainty and capture sequential dependencies in retinal features capabilities not achieved by conventional CNN or hybrid models.Using the Indian Diabetic Retinopathy Image Database (IDRiD), the proposed framework classifies DR into five clinically significant stages, from mild non-proliferative DR to proliferative DR.Experimental results demonstrate that FERNN achieves 89.20% accuracy and 85.44% precision, outperforming advanced baselines such as hybrid Inception-ResNet, DCGAN, and graph neural network models by up to 30.29%.These findings confirm that the proposed FERNN-BDOF framework provides a novel, robust, and scalable solution for automated DR detection, with strong potential for real-world clinical deployment.

Diabetic retinopathy (DR) is a complication of diabetic Mellitus, developing retinal lesions that impair vision. The DR detection in the early stages avoids permanent vision loss. The treatments provide relief, and the vision loss due to DR is irreversible. The manual grading of DR is time-consuming and prone to human errors. The other real-time problem is exchanging patient fundus image information with hospitals worldwide while upholding the organisations' privacy concerns. When training a deep learning (DL) network, two critical factors to keep in mind are creating a collaborative platform and protecting patient data privacy. Therefore, an automated DR detection technique is required while protecting patient data and privacy. In this work, we propose a novel DR severity grading technique based on Federated Learning (FL), a recent advancement in DL called DRFL. FL is a new research paradigm that allows DL models to be trained collectively without disclosing clinical information. In DRFL, we combined the Federated averaging (FedAvg) technique and the median of the categorical cross-entropy loss. Since in comparison to FedAvg, the median cross-entropy is better suited for either under-fitted or over-fitted clients. Also, we propose a novel central server that extracts multi-scale features from the fundus images to identify small lesions present in the fundus image. In this work, we consider five clients holding different preprocessed fundus images collected from publicly available databases such as MESSIDOR-2, IDRiD, Kaggle, and a local database collected from Silchar Medical College and Hospital. The proposed model obtained an accuracy of 98.6%, specificity of 99.3%, precision of 97.25%, and an F1 score of 97.5%, which are better results than other techniques.

Using Deep Learning Models to Characterize Major Retinal Features on Color Fundus Photographs

Purpose: To develop a new domain generalization (DG) framework for diabetic retinopathy (DR) classification using Vision Transformers (VTs). Methods: A deep-learning-based prediction-softening mechanism has been used to allow self-distillation, whereby the knowledge extracted is transferred to intermediate layers. Intermediate representations are further improved with an adaptive convex combination of one-hot labels and internal predictions of the model, which helps regularize the network and counteract overfitting. Various publicly available DR datasets, such as APTOS and EYEPACS, in multi-source and single-source DG settings containing three VT backbones (DeiT, T2T-VT, and CvT) were used. Results: The proposed framework outperforms current DG methods, with better top-1 accuracy, strong calibration and predictive accuracy in unfamiliar domains. Conclusion: The Softening Predictions for Self-Distillation (SPSD)-VT framework is a robust, reliable, and generalizable framework that classifies DR and underscores the important role that domain generalization plays in medical imaging and sets a standard for future studies.