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4채널 Multi-View 입력과 딥 러닝을 이용한 유방암 진단
- Alternative Title
- JiHoon Bae
- Author(s)
- 배지훈
- Alternative Author(s)
- JiHoon Bae
- Advisor
- 선우명훈
- Department
- 일반대학원 전자공학과
- Publisher
- The Graduate School, Ajou University
- Publication Year
- 2020-02
- Language
- eng
- Keyword
- Breast Cancer; Deep Learning; Mammography
- Alternative Abstract
- Medical image diagnosis should take into consideration the information contained in multiple images, not just a single image, such as natural image classification. Mammography is the most basic X-ray screening method for diagnosing breast cancer, and mammograms have four images per patient. Convolutional neural networks (CNN) should be able to diagnose using these four images. This paper proposes a 4-channel input CNN that simultaneously concatenates four images to solve the multi-view problem. CNN using the proposed 4-channel input has been trained and validated with the digital database for screening mammography (DDSM). The network has shown an area under the ROC curve (AUC) of 0.952 for the 2-class (positive vs negative) problem. In addition, this thesis proposes a new approach for the localization of lesions without patch labels or mask labels.
- Fulltext
- Appears in Collections:
- Graduate School of Ajou University > Department of Electronic Engineering > 3. Theses(Master)
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