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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전자공학회 IEIE Transactions on Smart Processing & Computing IEIE Transactions on Smart Processing & Computing Vol.10 No.2
- 발행연도
- 2021.4
- 수록면
- 101 - 108 (8page)
- DOI
- 10.5573/IEIESPC.2021.10.2.101
이용수
초록· 키워드
오류제보하기
The application of Artificial Intelligence (AI) in medicine has been a leading concern worldwide. Artificial intelligence-based systems not only support storing a large amount of data but also assist doctors in making a diagnosis. In addition, deep learning has obtained numerous achievements that greatly supported the development of image-based diagnostic methods. On the other hand, deep learning models still work as a black box that makes interpreting the output a challenge. Diagnosis based on images is currently a trend that plays a key role in clinical treatment by discovering abnormal regions for disease diagnosis. This paper proposes a computer-aid diagnosis system to support a pleural effusion diagnosis based on Chest X-ray (CXR) images. This study investigated several shallow convolutional neural network architectures that classify CXR images as well as the technique for processing imbalanced data using oversampling technology. The best model in the experiments was chosen to generate explanations using the Local Interpretable Model-agnostic Explanations (LIME) to support providing signals for pleural effusion diagnosis. The proposed method is expected to provide more informative CXR images of the pleural effusion diagnosis process.
목차
Abstract
1. Introduction
2. Related Work
3. The Proposed Scheme
4. Method
5. Results
6. Conclusion
References
참고문헌 (29)
참고문헌 신청
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Zhuang J., Cai J., Wang R., Zhang J., Zheng WS. (2020) Deep kNN for Medical Image Classification. In: Martel A.L. et al. (eds) Medical Image Computing and Computer Assisted Intervention - MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science, vol 12261. Springer, Cham. Article (CrossRef Link)
Xinran Li and Chenhui Xiang. Correlation-based Knearest neighbor algorithm. 2012 IEEE International Conference on Computer Science and Automation Engineering, Beijing, 2012, pp. 185-187, Article (CrossRef Link)
Pooch E.H.P., Ballester P., Barros R.C. (2020) Semisupervised Classification of Chest Radiographs. In: Cardoso J. et al. (eds) Interpretable and Annotation-Efficient Learning for Medical Image Computing. IMIMIC 2020, MIL3ID 2020, LABELS 2020. Lecture Notes in Computer Science, vol 12446. Springer, Cham. Article (CrossRef Link)
A.Inés, C.Domínguez, J.Heras, E.Mata, V.Pascual. Biomedical image classification made easier thanks to transfer and semi-supervised learning. Article (CrossRef Link)
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