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

arXiv: https://arxiv.org/abs/2502.14885 · HTML: https://mlsanity.com/paper/2502.14885/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 12 references that are in the map.
- First posted on arXiv 15 Feb 2025.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Medical Image Analysis using Convolutional Neural Networks: A Review](https://mlsanity.com/paper/1709.02250/) (2017)
- [Reliable Tuberculosis Detection using Chest X-ray with Deep Learning, Segmentation and Visualization](https://mlsanity.com/paper/2007.14895/) (2020)
- [Explainable Artificial Intelligence for Medical Applications: A Review](https://mlsanity.com/paper/2412.01829/) (2024)
- [MedAide: Leveraging Large Language Models for On-Premise Medical Assistance on Edge Devices](https://mlsanity.com/paper/2403.00830/) (2024)
- [S-E Pipeline: A Vision Transformer (ViT) based Resilient Classification Pipeline for Medical Imaging Against Adversarial Attacks](https://mlsanity.com/paper/2407.17587/) (2024)
- [Democratizing MLLMs in Healthcare: TinyLLaVA-Med for Efficient Healthcare Diagnostics in Resource-Constrained Settings](https://mlsanity.com/paper/2409.12184/) (2024)
- [Can Domain Experts Rely on AI Appropriately? A Case Study on AI-Assisted Prostate Cancer MRI Diagnosis](https://mlsanity.com/paper/2502.03482/) (2025)

## Abstract (arXiv)

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.