Overview

This book details the development of a deep learning model to automate the detection of diabetic maculopathy (DM), a leading cause of vision loss. Traditionally, diagnosing DM from retinal images is a time-consuming manual process. Using a MobileNet model trained on the Asia Pacific Tele-Ophthalmology Society (APTOS) dataset from Kaggle, the system can classify fundus images into different stages of DM severity. The significant step toward using AI for the automated diagnosis of diabetic eye diseases, with focus on testing the model in real-world clinical settings and adding explainability to its predictions to increase trust and understanding. This advancement highlights the growing role of deep learning in improving healthcare outcomes for diabetic patients.

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