AI 中文总结
本研究比较多种深度学习模型并新提出一种模型，用于基于NIH疟疾数据集的血液涂片检测疟疾，新模型达97.67%准确率，并通过XAI方法增强可解释性。
AI 中文摘要
超过80个国家报告了疟疾病例，造成61万人死亡，且预计这一数字还会增加。早期且准确地识别疟疾有助于挽救生命。诊断疟疾的有效方法是通过显微镜检查，但这种方法劳动强度大，需要具备特殊设备的专家。深度学习（DL）在医学诊断中已显示出有前景的结果。在此，我们探索了多种DL模型：ResNet18、MobileNetV2、EfficientNet-B2、VGG19，并提出了一个用于检测疟疾存在的模型，该模型使用来自NIH疟疾数据集的血液涂片。我们的实验表明，MobileNetV2以最小的模型大小（8.49 MB）和最快的推理时间（1.35 ms）达到了96.35%的准确率。所提出的模型达到了97.67%的准确率、0.9756的AUC，但推理时间最长（13.17 ms）。更大的架构输出更大的模型大小，但准确率中等。经过进一步剪枝，所提出的模型在准确率和推理时间上略有提升。GRAD-CAM、SHAP和LIME提供了模型的可解释人工智能（XAI）洞察。
英文摘要
More than 80 countries have reported malaria cases with 610 thousand deaths and are projected to increase. Identifying malaria early and accurately helps save lives and effective way to diagnose malaria is through microscopic methods that are labor intensive and require experts with special equipment. Deep learning (DL) has shown promising results in medical diagnosis. Here, we explored various DL models: ResNet18, MobileNetV2, EfficientNet-B2, VGG19 and proposed model ResNet18+TTA (ResNet18 backbone with modified classification head and test time augmentation) for detecting malaria presence using blood smears taken from the NIH Malaria dataset. Our experiment shows MobileNetV2 achieved 96.85 % accuracy with smallest model size (8.49 MB) and fastest inference (1.35 ms). The ResNet18+TTA model achieved 97.96 % accuracy, 0.996 AUC with longest inference time (13.32 ms). Larger architecture outputs a larger model size with moderate accuracy. Upon further pruning, ResNet18+TTA model gained a slight improvement in accuracy and reduced inference time. GRAD-CAM, SHAP and LIME provide explainable AI (XAI) insights into model predictions, using explanation agreement and divergence to evaluate predictive reliability.
Comments14 pages, 10 figures
