用低成本硬件实现高精度肺结核辅助诊断，适合基层医疗使用。
Pulmonary Tuberculosis Edge Diagnosis System Based on MindSpore Framework: Low-cost and High-precision Implementation with Ascend 310 Chip
- 基于MindSpore框架与Ascend310芯片，在边缘设备上部署MobileNetV3模型。
- 在4148张胸片上达到99.1%准确率，AUC达0.99。
- 整机成本低于150美元，适合资源匮乏地区的基层筛查。
肺结核（PTB）仍是全球公共卫生的重大挑战，尤其在医疗资源匮乏地区，专业诊疗知识和诊断工具严重不足。本文提出一种基于华为MindSpore框架与Ascend310边缘计算芯片的肺结核辅助诊断系统。采用MobileNetV3架构，结合Softmax交叉熵损失函数与动量优化器，在Orange Pie AIPro（Atlas 200 DK）边缘设备上以FP16混合精度运行。在包含4148张胸部影像的测试集上，模型准确率达到99.1%（AUC = 0.99），设备成本控制在150美元以内，为基层医疗机构提供了一种经济高效的AI辅助诊断方案。
原文摘要 · Abstract (English)
Pulmonary Tuberculosis (PTB) remains a major challenge for global health, especially in areas with poor medical resources, where access to specialized medical knowledge and diagnostic tools is limited. This paper presents an auxiliary diagnosis system for pulmonary tuberculosis based on Huawei MindSpore framework and Ascend310 edge computing chip. Using MobileNetV3 architecture and Softmax cross entropy loss function with momentum optimizer. The system operates with FP16 hybrid accuracy on the Orange pie AIPro (Atlas 200 DK) edge device and performs well. In the test set containing 4148 chest images, the model accuracy reached 99.1\% (AUC = 0.99), and the equipment cost was controlled within \$150, providing affordable AI-assisted diagnosis scheme for primary care.
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