Combining MEA-Net and LAP-Net for Pneumoconiosis Staging Framework

Objective 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. Methods 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. Results 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. Conclusion The proposed method can help doctors to identify the different stages of pneumoconiosis more accurately in the diagnosis of the disease.