A Novel Adaptive Hybrid Focal-Entropy Loss for Enhancing Diabetic Retinopathy Detection Using Convolutional Neural Networks

• URL: http://arxiv.org/abs/2411.10843v1
• Date: Sat, 16 Nov 2024 17:07:53 GMT
• Title: A Novel Adaptive Hybrid Focal-Entropy Loss for Enhancing Diabetic Retinopathy Detection Using Convolutional Neural Networks
• Authors: Pandiyaraju V, Santhosh Malarvannan, Shravan Venkatraman, Abeshek A, Priyadarshini B, Kannan A,
• Abstract summary: We propose Adaptive Hybrid Focal-Entropy Loss ( AHFE) for diabetic retinopathy detection. AHFE combines the ideas of focal loss and entropy loss with adaptive weighting in order to focus on minority classes. The state-of-the art models applied for diabetic retinopathy detection with AHFE revealed good performance improvements.
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• Abstract: Diabetic retinopathy is a leading cause of blindness around the world and demands precise AI-based diagnostic tools. Traditional loss functions in multi-class classification, such as Categorical Cross-Entropy (CCE), are very common but break down with class imbalance, especially in cases with inherently challenging or overlapping classes, which leads to biased and less sensitive models. Since a heavy imbalance exists in the number of examples for higher severity stage 4 diabetic retinopathy, etc., classes compared to those very early stages like class 0, achieving class balance is key. For this purpose, we propose the Adaptive Hybrid Focal-Entropy Loss which combines the ideas of focal loss and entropy loss with adaptive weighting in order to focus on minority classes and highlight the challenging samples. The state-of-the art models applied for diabetic retinopathy detection with AHFE revealed good performance improvements, indicating the top performances of ResNet50 at 99.79%, DenseNet121 at 98.86%, Xception at 98.92%, MobileNetV2 at 97.84%, and InceptionV3 at 93.62% accuracy. This sheds light into how AHFE promotes enhancement in AI-driven diagnostics for complex and imbalanced medical datasets.

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