Machine Learning Techniques for Automated Retinopathy Detection and Diagnosis

Abstract

The medical field requires blindness-related evidence which proves diabetic retinopathy non-preventable because it needs to be demonstrated through its initial occurrence and all subsequent occurrences. The machine learning (ML) automated detection systems can assist organizations to perform effective screenings that enable them to do their activities with fewer human considerations. The article examines the role of machine learning methods by the researchers to develop automated systems that detect various types of retinopathy. The study contrasts the conventional classification techniques with Support Vector Machines k-Nearest Neighbors Decision Trees random forests and most recent deep learning techniques that comprise Convolutional Neural Networks and transfer learning systems. The report indicates the key problems that encompass preprocessing images and feature extraction and dataset usage and performance assessment indicators. The analysis demonstrates that deep learning models are more capable of detecting intricate patterns on retinal images compared to conventional ones whereas the use of ensembles can optimize system performance and accuracy. The study explores three feasible issues that comprise data-imbalance and system-insight and clinical-implementation to demonstrate that the system with ML can introduce enhancements to the eye care delivery process.