Application of Machine Learning to the Detection of Retinal Diseases - Regin, R.; Rajest, S. Suman; T, Shynu; R, Steffi. (European Journal of Life Safety and Stability (2660-9630), 2024)
In this study, we use a machine learning method to identify cases of diabetic retinopathy in humans. The suggested approach uses classification algorithms on various variables from an existing Diabetic Retinopathy dataset, such as optical disc diameter, lesion-specific information (microaneurysms, exudates, or presence of haemorrhages), and so on. Following feature extraction, the presence of diabetic retinopathy may be predicted. A Decision Tree, a Logistic Regression, and a Support Vector Machine were all utilised in the proposed system's prediction process. Results from the current works were significantly worse than the suggested method's 88% accuracy. Using the SVM method, it can detect the existence of diabetic retinopathy, macular degeneration, myopia, and other retinal illnesses. The next step is to sort them according to their hue and morphological assumptions. For improved accuracy, the system is classified using an approach that combines Decision Trees with Logistic Regression and Support Vector Machines.