Deep Learning Pneumoconiosis Staging and Diagnosis System Based on Multi-stage Joint Approach

Abstract

Abstract Background: Pneumoconiosis has a significant impact on the quality of patient survival due to its difficult staging diagnosis and poor prognosis. This study aims to develop a computer-aided diagnostic system for the screening and staging pneumoconiosis based on a multi-stage joint deep learning approach using X-ray chest radiographs of pneumoconiosis patients. Methods: In this study, a total of 498 medical chest radiographs, from the Department of Radiology of West China Fourth Hospital. The dataset was randomly divided into a training set and a test set at a ratio of 4:1. Following histogram equalization for image enhancement, the images were segmented using the U-Net model and staging was predicted using a convolutional neural network classification model. We first used Efficient-Net for multi-classification staging diagnosis, but the results showed that stage I/II of pneumoconiosis was difficult to diagnose. Therefore, based on clinical practice we continued to improve the model by using Res-Net 34 Multi-stage joint method. Results: Of the 498 cases collected, the classification model using the Efficient-Net achieved an accuracy of 83% with a Quadratic Weighted Kappa (QWK) score of 0.889. The classification model using the multi-stage joint approach of Res-Net 34 achieved an accuracy of 89% with an area under the curve(AUC) of 0.98 and a high QWK score of 0.94. Conclusions: In this study, the diagnostic accuracy of pneumoconiosis staging was significantly improved by an innovative combined multi-stage approach, which provides a reference for clinical application and pneumoconiosis screening.