상세정보
학술대회
A study on deep learning-based classification for Pneumonia detection
- 발행일
- 202210
- 출처
- International Conference on Information and Communication Technology Convergence (ICTC) 2022, pp.1-3
- 협약과제
- 22HS2800, 신경망 응용 자동생성 및 실행환경 최적화 배포를 지원하는 통합개발 프레임워크 기술개발, 조창식
- 초록
- In this paper, we investigate the various aspects of methodologies in deep learning-based pneumonia classification using chest x-ray images. As widely known, selecting appropriate hyper-parameters is essential for increasing the classification performance in convolution neural networks(CNN). We experiment with various hyper-parameters, including the number of layers, optimizer, learning rate, and momentum factor for diagnosing pneumonia using CNN. In addition, we test different CNN models and augmentation methods for chest x-ray diagnosing. Experimental results show that the proposed non-rigid transform based on augmentation increases classification accuracy by up to 5%.
- KSP 제안 키워드
- Chest X Ray(CXR), Classification Performance, Convolution neural network(CNN), Learning rate, Learning-Based classification, Momentum Factor, Number of layers, classification accuracy, deep learning(DL), hyper-parameters, non-rigid