Combining MEA-Net and LAP-Net for Pneumoconiosis Staging Framework.
Authors
Affiliations (3)
Affiliations (3)
- Department of Artificial Intelligence and Computer Engineering, National Chin-Yi University of Technology, Taichung, Taiwan.
- Program in Environmental and Occupational Medicine, and Research Center for Precision Environmental Medicine, College of Medical, Kaohsiung Medical University, Kaohsiung, Taiwan.
- Department of Information Management, National Chung Hsing University, Taichung, Taiwan.
Abstract
Pneumoconiosis is a common and highly hazardous occupational disease. The staging of pneumoconiosis is mainly carried out by experienced doctors on the basis of the shadows and textures on lung X-rays. Despite well-defined criteria, the process remains influenced by individual clinical judgment. In order to improve the subjective process of pneumoconiosis diagnosis, this study proposes a new deep learning framework for pneumoconiosis staging framework using MEA-Net and LAP-Net. The experimental results show that the accuracy, precision, recall, specificity, F1-score and AUC in the four-stage classification reached 95.24%, 95.15%, 95.15%, 90.58%, 94.85% and 98.87%, respectively. The proposed method can help doctors to identify the different stages of pneumoconiosis more accurately in the diagnosis of the disease.