用自研CNN实现96%准确率的疟疾自动诊断，支持可解释性分析。
A CNN-Based Malaria Diagnosis from Blood Cell Images with SHAP and LIME Explainability
- 设计专用CNN模型自动分类血细胞图像是否感染
- 准确率达96%，精确率与召回率均超0.95
- 结合SHAP/LIME提升模型决策可解释性，适合资源匮乏地区
疟疾在热带和亚热带地区仍是重大健康问题，由按蚊传播的疟原虫引起。传统诊断方法如显微镜血涂片分析灵敏度低、依赖专家判断，且在偏远地区难以获取。为此，本研究提出一种基于深度学习的方案，采用自研卷积神经网络（CNN）自动分类血细胞图像为寄生虫感染或未感染。模型准确率达96%，各类别精确率与召回率均超过0.95。研究还对比了ResNet50、VGG16、MobileNetV2和DenseNet121等主流架构。为增强可解释性，应用SHAP、LIME和梯度显著图等可解释AI技术。结果表明，该系统可在资源有限地区提供快速、精准且可理解的疟疾诊断。
原文摘要 · Abstract (English)
Malaria remains a prevalent health concern in regions with tropical and subtropical climates. The cause of malaria is the Plasmodium parasite, which is transmitted through the bites of infected female Anopheles mosquitoes. Traditional diagnostic methods, such as microscopic blood smear analysis, are low in sensitivity, depend on expert judgment, and require resources that may not be available in remote settings. To overcome these limitations, this study proposes a deep learning-based approach utilizing a custom Convolutional Neural Network (CNN) to automatically classify blood cell images as parasitized or uninfected. The model achieves an accuracy of 96%, with precision and recall scores exceeding 0.95 for both classes. This study also compares the custom CNN with established deep learning architectures, including ResNet50, VGG16, MobileNetV2, and DenseNet121. To enhance model interpretability, Explainable AI techniques such as SHAP, LIME, and Saliency Maps are applied. The proposed system shows how deep learning can provide quick, accurate and understandable malaria diagnosis, especially in areas with limited resources.
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