삼차원 심층 콘볼루션 신경망을 이용한 컴퓨터 단층 촬영 영상 내 폐 결절 분류

국문 초록 (Abstract)

전 세계 암 발병의 큰 비중을 차지하는 폐암을 조기에 예방하기 위해서는 폐 결절을 찾아내 악성 여부를 검사해야 한다. 본 연구에서는 삼차원 시층 콘볼루션 신경망을 이용해 결절의 악성 여부를 판단하는 모델을 제안한다. 숏컷 연결을 이용한 모델을 사용했고, 분류 성능 향상을 위해 앙상블 기법을 이용한다. 본 모델을 LUng Nodule Analysis 2016 대회 데이터에 적용하여 모델의 성능을 측정하고 정확도를 검증한다. 본 모델은 대회의 평가 지표인 Competition Performance Metric 기준 0.899를 기록하였고, 이는 기존 참가자들의 성능과 비교하였을 때 우수한 결과이다.

다국어 초록 (Multilingual Abstract)

Early detection and examination of pulmonary nodules is the most effective ways to prevent lung cancer, accounting for more than a quarter of all cancer deaths. In this paper, we propose a 3D deep convolutional neural network for pulmonary nodule recognition. We use deep convolutional neural network that uses shortcut connections and the ensemble method is used to boost recognition performance. Proposed models are trained and tested on Lung Nodule Analysis 2016 competition dataset. We evaluate performance of models and verify preciseness. Proposed model produces 0.899 of Competition Performance Metric value, that is evaluation criteria of competition. It is outperforming value than that of other participants.

목차 (Table of Contents)

• 요약
• Abstract
• 1. 서론
• 2. 방법론
• 3. 실험
• 4. 결론 및 향후 연구
• References

참고문헌 (Reference)

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