Pulmonary Tuberculosis Edge Diagnosis System Based on MindSpore Framework: Low-cost and High-precision Implementation with Ascend 310 Chip
This provides an affordable AI-assisted diagnosis solution for primary care in regions with poor medical resources, though it is incremental as it applies existing methods (MobileNetV3) to a new domain.
The paper tackled the problem of diagnosing pulmonary tuberculosis in resource-limited areas by developing a low-cost edge computing system using the MindSpore framework and Ascend 310 chip, achieving 99.1% accuracy and an AUC of 0.99 on a test set of 4148 chest images with equipment costs under $150.
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.