Design of an Iterative Q Learning Model for Multistage Classification
of Diabetic Retinopathy & Glaucoma

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Design of an Iterative Q Learning Model for Multistage Classification of Diabetic Retinopathy & Glaucoma

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Abstract

This study addresses the escalating prevalence of diabetic retinopathy (DR) and glaucoma, major global causes of vision impairment. To improve diagnostic accuracy and classification speed, we propose an innovative iterative Q Learning model integrated with Fuzzy C Means clustering. Traditional diagnostic frameworks often struggle with accuracy and delay in disease stage classification, particularly in discerning complex features like exudates and veins. Our model overcomes these challenges by combining Fuzzy C Means with Q Learning, enhancing precision in identifying key retinal components. The core of our approach is a custom-designed 45- layer 2D Convolutional Neural Network (CNN) optimized for nuanced detection of DR and glaucoma stages. Performance on IDRID and SMDG-19 datasets and their samples demonstrates a 10.9% higher precision, 8.5% increased overall accuracy, 8.3% improved recall, 10.4% higher Area under the Curve (AUC), 5.9% enhanced specificity, and a 2.9% reduction in delay compared to existing methods. This model has transformative potential in revolutionizing DR and glaucoma diagnostics, paving the way for timely medical interventions and potentially reducing vision loss. The integration of advanced machine learning with medical imaging sets a precedent for future research in ophthalmology and beyond in clinical scenarios.

Keywords

Convolutional Neural Networks Diabetic Retinopathy Fuzzy C Means Glaucoma

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