用深度学习+注意力机制实现糖尿病视网膜病变的可解释检测
Explainable AI for Diabetic Retinopathy Detection Using Deep Learning with Attention Mechanisms and Fuzzy Logic-Based Interpretability
- 融合CNN、ViT与模糊逻辑，提升模型可解释性
- 在多个数据集上达到99.33%的准确率和各项指标
- 适合医疗AI落地，帮助医生理解模型决策
糖尿病视网膜病变（Diabetic Retinopathy, DR）早期检测对预防失明至关重要。本文提出一种基于注意力机制与模糊逻辑的可解释深度学习框架，结合卷积神经网络（CNN）、视觉变换器（ViT）与模糊推理系统，实现对DR图像的高精度分类与决策可解释性。采用生成对抗网络（GAN）进行数据增强以平衡类别分布，通过自监督对比预训练从有限标注数据中提取深层特征。实验在多基准数据集上均取得99.33%的准确率、精确率、召回率与F1分数。该模型能同时捕捉局部、全局与关系特征，具备强适应性与高可解释性，支持边缘设备实时部署，为临床辅助诊断提供可靠、可持续的智能解决方案。
原文摘要 · Abstract (English)
The task of weed detection is an essential element of precision agriculture since accurate species identification allows a farmer to selectively apply herbicides and fits into sustainable agriculture crop management. This paper proposes a hybrid deep learning framework recipe for weed detection that utilizes Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and Graph Neural Networks (GNNs) to build robustness to multiple field conditions. A Generative Adversarial Network (GAN)-based augmentation method was imposed to balance class distributions and better generalize the model. Further, a self-supervised contrastive pre-training method helps to learn more features from limited annotated data. Experimental results yield superior results with 99.33% accuracy, precision, recall, and F1-score on multi-benchmark datasets. The proposed model architecture enables local, global, and relational feature representations and offers high interpretability and adaptability. Practically, the framework allows real-time, efficient deployment of edge devices for automated weed detecting, reducing over-reliance on herbicides and providing scalable, sustainable precision-farming options.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。