Deep Learning Algorithms with LIME and Similarity Distance Analysis on COVID-19 Chest X-ray Dataset

Kuan-Yung Chen, Hsi-Chieh Lee (), Tsung-Chieh Lin, Chih-Ying Lee and Zih-Ping Ho
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Kuan-Yung Chen: Department of Radiology, Chang Bing Show Chwan Memorial Hospital, Changhua 505, Taiwan
Hsi-Chieh Lee: Department of Computer Science and Information Engineering, National Quemoy University, Kinmen County 892, Taiwan
Tsung-Chieh Lin: Department of Computer Science and Information Engineering, National Quemoy University, Kinmen County 892, Taiwan
Chih-Ying Lee: College of Bioresources and Agriculture, National Taiwan University, Taipei 106, Taiwan
Zih-Ping Ho: Department of Business Administration, Chihlee University of Technology, New Taipei City 220, Taiwan

IJERPH, 2023, vol. 20, issue 5, 1-14

Abstract: In the last few years, many types of research have been conducted on the most harmful pandemic, COVID-19. Machine learning approaches have been applied to investigate chest X-rays of COVID-19 patients in many respects. This study focuses on the deep learning algorithm from the standpoint of feature space and similarity analysis. Firstly, we utilized Local Interpretable Model-agnostic Explanations (LIME) to justify the necessity of the region of interest (ROI) process and further prepared ROI via U-Net segmentation that masked out non-lung areas of images to prevent the classifier from being distracted by irrelevant features. The experimental results were promising, with detection performance reaching an overall accuracy of 95.5%, a sensitivity of 98.4%, a precision of 94.7%, and an F1 score of 96.5% on the COVID-19 category. Secondly, we applied similarity analysis to identify outliers and further provided an objective confidence reference specific to the similarity distance to centers or boundaries of clusters while inferring. Finally, the experimental results suggested putting more effort into enhancing the low-accuracy subspace locally, which is identified by the similarity distance to the centers. The experimental results were promising, and based on those perspectives, our approach could be more flexible to deploy dedicated classifiers specific to different subspaces instead of one rigid end-to-end black box model for all feature space.

Keywords: COVID-19; LIME (Local Interpretable Model-agnostic Explanations); feature space; machine learning; outlier; PCA; similarity distance; U-Net segmentation (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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