Pulmonary Tuberculosis Edge Diagnosis System Based on MindSpore Framework: Low-cost and High-precision Implementation with Ascend 310 Chip

TL;DR

This paper introduces a low-cost, high-precision pulmonary tuberculosis diagnosis system leveraging Huawei's MindSpore framework and Ascend 310 chip, achieving 99.1% accuracy on chest images.

Contribution

It presents a novel edge computing-based PTB diagnosis system using MobileNetV3 on Ascend 310, combining affordability with high diagnostic accuracy.

Findings

Model accuracy of 99.1% on test set

System cost within $150

Effective AI-assisted diagnosis in resource-limited settings

Abstract

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.