An Overview of Deep Learning Methods for Exudate Detection in Diabetic Retinopathy

Abstract

Background: This comprehensive review aims to provide a thorough overview of exudate detection techniques with a focus on their application in diagnosing diabetic retinopathy(DR) early. Main body of the abstract: This review employs a systematic analysis of peer-reviewed articles, investigating the utilization of deep learning techniques in exudate detection. These techniques encompass convolutional neural networks, fuzzy c-means clustering, neural networks, and more. The precise detection and quantification of exudates are pivotal in monitoring the progression of DR, as they serve as crucial indicators for assessing the risk factors associated with vision-threatening complications. Conventional methods are prone to erroneous clinical decisions due to factors like observer fatigue and subjectivity during interpretation. Consequently, an increasing number of deep learning-based approaches have emerged to address these limitations. Short conclusion: The techniques for detecting diabetic retinopathy exudates demonstrate considerable promise in terms of accuracy and efficiency. Nonetheless, further research is imperative to develop more robust and reliable methods, facilitating early diagnosis and timely intervention in cases of DR.