Diabetic Retinopathy Diagnosis Categorization Using Deep Learning

Diabetic Retinopathy (DR) is an eye disease associated with chronic diabetes. DR is the leading cause of blindness among working aged adults around the world and estimated it may affect more than 93 million people. Progression to vision impairment can be slowed or controlled if DR is detected in time, however this can be difficult as the disease often shows few symptoms until it is too late to provide effective treatment. Currently, detecting DR is a time-consuming and manual process, which requires an ophthalmologist or trained clinician to examine and evaluate digital color fundus photographs of the retina, to identify DR by the presence of lesions associated with the vascular abnormalities caused by the disease. The automated method of DR screening will speed up the detection and decision-making process, which will help to control or manage DR progression. This paper presents an automated classification system, in which it analyzes fundus images with varying illumination and fields of view and generates a severity grade for diabetic retinopathy (DR) using machine learning models such as CNN, VGG-16 and VGG-19.This system achieves 80% sensitivity, 82% accuracy, 82% specificity, and 0.904 AUC for classifying images into 5 categories ranging from 0 to 4, where 0 is no DR and 4 is proliferative DR

Optimized Screening of Glaucoma using Fundus Images and Deep Learning

Diabetic retinopathy, glaucoma, and age-related macular degeneration are among the leading causes of global visual loss. Early detection and diagnosis of these conditions are crucial to reduce vision loss and improve patient outcomes. In recent years, deep learning algorithms have shown great potential in automating the diagnosis and categorization of eye disorders using medical photos. For this purpose, the ResNet-50 architecture is employed in a deep learning-based strategy. The approach involves fine-tuning a pre-trained ResNet-50 model using over 5,000 retinal pictures from the ODIR dataset, covering ten different ocular diseases. To enhance the model's generalization performance and avoid overfitting, various data augmentation techniques are applied to the training data. The model successfully detects glaucoma-related ocular illnesses, including cataract, diabetic retinopathy, and healthy eyes. Performance evaluation using metrics like accuracy, precision, recall, and F1-score shows that the model achieved 92.60% accuracy, 93.54% precision, 91.60% recall, and an F1-score of 91.68%. These results indicate that the proposed strategy outperforms many state-of-the-art approaches in the detection and categorization of eye disorders. This success underscores the potential of deep learning-based methods in automated ocular illness identification, facilitating early diagnosis and timely treatment to ultimately improve patient outcomes

Methods for Exploiting High-Resolution Imagery for Deep Learning-Based Diabetic Retinopathy Detection and Grading

Diabetic retinopathy is a disease that affects the eyes of people with diabetes, and it can cause blindness. To diagnose diabetic retinopathy, ophthalmologists image the back surface of the inside of the eye, a process referred to as fundus photography. Ophthalmologists must then diagnose and grade the severity of diabetic retinopathy by analyzing details in the image, which can be difficult and time-consuming. Alternatively, due to the availability of labeled datasets containing fundus images with diabetic retinopathy, AI methods like deep learning can provide automated detection and grading algorithms. We show that the resolution of an image has a large effect on the accuracy of grading algorithms. So, we study several techniques to increase the accuracy of the algorithm by taking advantage of higher-resolution data, including using a region of interest as the input and applying an image transformation to make the circular fundus image square. While none of our proposed methods result in an increase in performance for grading diabetic retinopathy, the circle to square transformation results in an increase in accuracy and AUC for detection of diabetic retinopathy. This work provides a useful starting point for future research aimed at increasing the resolution content in a fundus image

A Review on Detection of Diabetic Retinopathy using Deep Learning and Transfer Learning based Strategies

Diabetic Retinopathy (DR) is considered to be one of the most widely observed and a complex variation of diabetes and stands as a leading cause of blindness globally. The occurrence of DR causes impairment in the retinal blood vessels and leads to unusual growth of blood arteries in the eye. Manual examinations and analysis suggests that the prevalence of DR has been enormously growing at an exponential rate and has already registered for more than 160 million cases worldwide. On the other hand, its diagnostic screening is not only challenging, but also computationally expensive at the same time. Due to the highlighting importance of its early diagnosis in terms of treatment, multiple concepts to DR detection have been used in the past few years. However, research in recent times has resulted in the fact that deep learning based CNN structures and Transfer Learning based MedNets have been popularly used in DR detection, due to its superior performance in the medical domain. As a result of such advancements in Deep Learning methodologies, this article proposes a review on automated approaches used to detect diabetic retinopathy using image processing and disease classification techniques. The review is further preceded with a comprehensive analysis on training a model with an already pre-trained network whose primary goal is to generate useful information and provide it to diabetic researchers, medical practitioners and patients

Leveraging Semi-Supervised Graph Learning for Enhanced Diabetic Retinopathy Detection

Diabetic Retinopathy (DR) is a significant cause of blindness globally, highlighting the urgent need for early detection and effective treatment. Recent advancements in Machine Learning (ML) techniques have shown promise in DR detection, but the availability of labeled data often limits their performance. This research proposes a novel Semi-Supervised Graph Learning SSGL algorithm tailored for DR detection, which capitalizes on the relationships between labelled and unlabeled data to enhance accuracy. The work begins by investigating data augmentation and preprocessing techniques to address the challenges of image quality and feature variations. Techniques such as image cropping, resizing, contrast adjustment, normalization, and data augmentation are explored to optimize feature extraction and improve the overall quality of retinal images. Moreover, apart from detection and diagnosis, this work delves into applying ML algorithms for predicting the risk of developing DR or the likelihood of disease progression. Personalized risk scores for individual patients are generated using comprehensive patient data encompassing demographic information, medical history, and retinal images. The proposed Semi-Supervised Graph learning algorithm is rigorously evaluated on two publicly available datasets and is benchmarked against existing methods. Results indicate significant improvements in classification accuracy, specificity, and sensitivity while demonstrating robustness against noise and outlie rs.Notably, the proposed algorithm addresses the challenge of imbalanced datasets, common in medical image analysis, further enhancing its practical applicability.Comment: 13 pages, 6 figure

Deep learning for diabetic retinopathy detection and classification based on fundus images: A review.

Diabetic Retinopathy is a retina disease caused by diabetes mellitus and it is the leading cause of blindness globally. Early detection and treatment are necessary in order to delay or avoid vision deterioration and vision loss. To that end, many artificial-intelligence-powered methods have been proposed by the research community for the detection and classification of diabetic retinopathy on fundus retina images. This review article provides a thorough analysis of the use of deep learning methods at the various steps of the diabetic retinopathy detection pipeline based on fundus images. We discuss several aspects of that pipeline, ranging from the datasets that are widely used by the research community, the preprocessing techniques employed and how these accelerate and improve the models' performance, to the development of such deep learning models for the diagnosis and grading of the disease as well as the localization of the disease's lesions. We also discuss certain models that have been applied in real clinical settings. Finally, we conclude with some important insights and provide future research directions

Explainable artificial intelligence (XAI) in deep learning-based medical image analysis

With an increase in deep learning-based methods, the call for explainability of such methods grows, especially in high-stakes decision making areas such as medical image analysis. This survey presents an overview of eXplainable Artificial Intelligence (XAI) used in deep learning-based medical image analysis. A framework of XAI criteria is introduced to classify deep learning-based medical image analysis methods. Papers on XAI techniques in medical image analysis are then surveyed and categorized according to the framework and according to anatomical location. The paper concludes with an outlook of future opportunities for XAI in medical image analysis.Comment: Submitted for publication. Comments welcome by email to first autho

Análisis de retinografías basado en Deep Learning para la ayuda al diagnóstico de la retinopatía diabética

La Retinopatía Diabética (RD) es una complicación de la diabetes y es la causa más frecuente de ceguera en la población laboral activa de los paísesdesarrollados. Cuando se trata de forma precoz, la pérdida de visión se puede prevenir. Para ello, es necesario que los pacientes se sometan aexámenes oftalmológicos regulares en los que se capturan y analizan imágenes de su fondo ocular o retinografías. No obstante, la creciente incidencia dela diabetes y la falta de profesionales sanitarios dificultan la detección precoz de la RD. En este contexto, los sistemas automáticos de ayuda aldiagnóstico de la RD ofrecen beneficios en escenarios clínicos y de cribado. En este TFM se pretende contribuir a esta tarea mediante el desarrollo de unmétodo automático de procesado de retinografías basado en técnicas de deep learning. Para ello se empleará el lenguaje de programación Python y setrabajará con retinografías procedentes de un contexto clínico real. Asimismo, la alumna tendrá la oportunidad de trabajar en un grupo de investigaciónmultidisciplinar, colaborando con ingenieros y médicos especialistas en oftalmología del Hospital Clínico Universitario de Valladolid.Sight is one of the most important senses for human beings. In recent years, the number of eye diseases has increased considerably and the same trend is expected in the coming years. Some of them, such as diabetic retinopathy, glaucoma or cataracts, have become major causes of vision loss worldwide. The alterations they cause in the human eye can be seen using digital images, such as fundus images. This technique is very common and useful for the diagnosis of this type of pathologies. Early detection is key to prevent the disease from reaching its most advanced stages and to make treatment more effective. Therefore patients should undergo frequent ophthalmological examinations. However, the increasing incidence of some diseases and the shortage of specialist ophthalmologists make the manual analysis of retinal images a complex and time-consuming task. In this context, automated screening systems can be very useful to assist ophthalmologists. Despite the great effectiveness of Deep learning-based systems, their application in clinical practice is still not very evident, as a consequence of their "black box" nature. In order to solve this problem, Explainable Artificial Intelligence (XAI) has been developed, a set of techniques that try to explain the decisions made by computational models when they are used for a specific task.Departamento de Teoría de la Señal y Comunicaciones e Ingeniería TelemáticaMáster en Ingeniería de Telecomunicació