针对糖尿病视网膜病变检测中的类别不平衡问题，提出自适应混合焦点-熵损失函数。
A Novel Adaptive Hybrid Focal-Entropy Loss for Enhancing Diabetic Retinopathy Detection Using Convolutional Neural Networks
- 融合焦点损失与熵损失，动态调整权重以关注少数类和难分类样本。
- 在DR数据集上，ResNet50准确率达99.79%，显著提升模型对重症病例的敏感性。
- 适用于医疗图像中类别严重失衡场景，尤其适合医学AI诊断系统优化。
糖尿病视网膜病变是全球致盲的主要原因，亟需高精度的AI辅助诊断工具。传统多分类损失函数如类别交叉熵（CCE）在类别不平衡时表现不佳，尤其在早期阶段（如类别0）与重度阶段（如类别4）样本数量差异大时，易导致模型偏向多数类。为此，本文提出自适应混合焦点-熵损失（AHFE），结合焦点损失与熵损失的优势，并引入自适应加权机制，增强对少数类和难分类样本的关注。在主流模型上测试，结果表明：ResNet50达到99.79%准确率，DenseNet121为98.86%，Xception为98.92%，MobileNetV2为97.84%，InceptionV3为93.62%。实验验证了该损失函数在复杂、不平衡医疗数据上的有效性，为医学AI诊断提供了新思路。
原文摘要 · Abstract (English)
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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