Grading and Anomaly Detection for Automated Retinal Image Analysis using Deep Learning
This research addresses the problem of early detection and diagnosis of diabetic retinopathy to prevent blindness in diabetic patients, but it is incremental as it reviews existing methods rather than introducing new ones.
The study conducted a systematic review of 62 articles to explore deep learning techniques, including CNN-based models and ensemble learning, for grading and anomaly detection in diabetic retinopathy, showing that ensemble methods yield high specificity and superior performance compared to individual models.
The significant portion of diabetic patients was affected due to major blindness caused by Diabetic retinopathy (DR). For diabetic retinopathy, lesion segmentation, and detection the comprehensive examination is delved into the deep learning techniques application. The study conducted a systematic literature review using the PRISMA analysis and 62 articles has been investigated in the research. By including CNN-based models for DR grading, and feature fusion several deep-learning methodologies are explored during the study. For enhancing effectiveness in classification accuracy and robustness the data augmentation and ensemble learning strategies are scrutinized. By demonstrating the superior performance compared to individual models the efficacy of ensemble learning methods is investigated. The potential ensemble approaches in DR diagnosis are shown by the integration of multiple pre-trained networks with custom classifiers that yield high specificity. The diverse deep-learning techniques that are employed for detecting DR lesions are discussed within the diabetic retinopathy lesions segmentation and detection section. By emphasizing the requirement for continued research and integration into clinical practice deep learning shows promise for personalized healthcare and early detection of diabetics.