Deep Learning in Multi-Class Lung Diseases' Classification on Chest X-ray Imagesopen access
- Authors
- Kim, Sungyeup; Rim, Beanbonyka; Choi, Seongjun; Lee, Ahyoung; Min, Sedong; Hong, Min
- Issue Date
- Apr-2022
- Publisher
- MDPI AG
- Keywords
- multi-class classification; deep learning; transfer learning; EfficientNet v2; chest X-ray image
- Citation
- Diagnostics, v.12, no.4, pp 1 - 24
- Pages
- 24
- Journal Title
- Diagnostics
- Volume
- 12
- Number
- 4
- Start Page
- 1
- End Page
- 24
- URI
- https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/20730
- DOI
- 10.3390/diagnostics12040915
- ISSN
- 2075-4418
- Abstract
- Chest X-ray radiographic (CXR) imagery enables earlier and easier lung disease diagnosis. Therefore, in this paper, we propose a deep learning method using a transfer learning technique to classify lung diseases on CXR images to improve the efficiency and accuracy of computer-aided diagnostic systems' (CADs') diagnostic performance. Our proposed method is a one-step, end-to-end learning, which means that raw CXR images are directly inputted into a deep learning model (EfficientNet v2-M) to extract their meaningful features in identifying disease categories. We experimented using our proposed method on three classes of normal, pneumonia, and pneumothorax of the U.S. National Institutes of Health (NIH) data set, and achieved validation performances of loss = 0.6933, accuracy = 82.15%, sensitivity = 81.40%, and specificity = 91.65%. We also experimented on the Cheonan Soonchunhyang University Hospital (SCH) data set on four classes of normal, pneumonia, pneumothorax, and tuberculosis, and achieved validation performances of loss = 0.7658, accuracy = 82.20%, sensitivity = 81.40%, and specificity = 94.48%; testing accuracy of normal, pneumonia, pneumothorax, and tuberculosis classes was 63.60%, 82.30%, 82.80%, and 89.90%, respectively.
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- Appears in
Collections - College of Medical Sciences > Department of Medical IT Engineering > 1. Journal Articles
- College of Medicine > Department of Otorhinolaryngology > 1. Journal Articles
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