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Combining MEA-Net and LAP-Net for Pneumoconiosis Staging Framework
Yen-Yu Chen1, Hung-Yi Chuang2, Hsien-Chu Wu1
1From the Department of Artificial Intelligence and Computer Engineering, National Chin-Yi University of Technology, Taichung, Taiwan (Y.-Y.C., H.-C.W.).
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-ray films. Despite well-defined criteria, the process remains influenced by individual clinical judgment.
Methods:
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 area under the curve 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.
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