지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2022.11
- 수록면
- 117 - 125 (9page)
이용수
초록· 키워드
오류제보하기
COVID-19 is a pathogen called SARS-CoV-2, an RNA virus that can infect various animals, including humans. As COVID-19 spread globally, the World Health Organization upgraded it to a pandemic in March 2020. In addition to solving the problem of shortage of medical personnel, rapid and accurate classification of infected patients emerged as an important issue. Therefore, we propose a deep learning-based chest X-ray image reading model that can notify the doctor whether the patient is infected. The goal is to achieve multiclass classification, which not only classifies COVID-19 infections, but also other lung diseases to help the medical community. The proposed method is a combination model. It involves pre-processing the chest X-ray image using the image augmentation method and various convolutional neural network (CNN) models. The purpose of the proposed method is to classify COVID-19, normal people, and viral pneumonia appropriately. Overall, 15,153 X-ray images were used in the study. By using the proposed method, we obtained a model with high accuracy through improved image data. Characteristically, some models tend to detect COVID-19 and pneumonia properly. Finally, an ensemble model was created using models made by the proposed method. Eventually, we obtained a high accuracy (0.981) model for detecting infections appropriately.
목차
ABSTRACT
1. Introduction
2. Methods
3. Results and Discussion
4. Conclusions
References
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