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4채널 Multi-View 입력과 딥 러닝을 이용한 유방암 진단
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 선우명훈 | - |
| dc.contributor.author | 배지훈 | - |
| dc.date.accessioned | 2022-11-29T02:31:59Z | - |
| dc.date.available | 2022-11-29T02:31:59Z | - |
| dc.date.issued | 2020-02 | - |
| dc.identifier.other | 29758 | - |
| dc.identifier.uri | https://dspace.ajou.ac.kr/handle/2018.oak/19474 | - |
| dc.description | 학위논문(석사)--아주대학교 일반대학원 :전자공학과,2020. 2 | - |
| dc.description.tableofcontents | I. Introduction 1 II. Method 6 A. Deep Convolutional Neural Networks 6 B. Multi-view Problems and Proposed 4-Channel Input 8 C. Sequential Phase Learning 10 D. Visualization 13 III. Experiments 16 A. Dataset 16 B. High Resolution Image Input 16 C. Experimental Setup 17 IV. Experimental Results 19 V. Conclusion 23 Bibliography 24 | - |
| dc.language.iso | eng | - |
| dc.publisher | The Graduate School, Ajou University | - |
| dc.rights | 아주대학교 논문은 저작권에 의해 보호받습니다. | - |
| dc.title | 4채널 Multi-View 입력과 딥 러닝을 이용한 유방암 진단 | - |
| dc.title.alternative | JiHoon Bae | - |
| dc.type | Thesis | - |
| dc.contributor.affiliation | 아주대학교 일반대학원 | - |
| dc.contributor.alternativeName | JiHoon Bae | - |
| dc.contributor.department | 일반대학원 전자공학과 | - |
| dc.date.awarded | 2020. 2 | - |
| dc.description.degree | Master | - |
| dc.identifier.localId | 1138496 | - |
| dc.identifier.uci | I804:41038-000000029758 | - |
| dc.identifier.url | http://dcoll.ajou.ac.kr:9080/dcollection/common/orgView/000000029758 | - |
| dc.subject.keyword | Breast Cancer | - |
| dc.subject.keyword | Deep Learning | - |
| dc.subject.keyword | Mammography | - |
| dc.description.alternativeAbstract | 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. | - |
- Appears in Collections:
- Graduate School of Ajou University > Department of Electronic Engineering > 3. Theses(Master)
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