학술논문
Evaluation and Research of Chest X-ray Image Recognition in the Machine Learning
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- 영문명
- 발행기관
- 한국방사선학회
- 저자명
- 김채곤 이선학 한기은 김준석 김재희 김은선 이건호 조윤진 박재홍
- 간행물 정보
- 『한국방사선학회 학술대회 논문집』한국방사선학회 2017년 추계종합학술대회논문집, 92~92쪽, 전체 1쪽
- 주제분류
- 공학 > 기타공학
- 파일형태
- 발행일자
- 2017.11.10
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이 학술논문 정보는 (주)교보문고와 각 발행기관 사이에 저작물 이용 계약이 체결된 것으로, 교보문고를 통해 제공되고 있습니다.
국문 초록
4차 산업 혁명이 도래함에 따라 인공지능 분야는 매우 빠르게 발전하고 있다. 특히 기계학습은 컴퓨터 하드웨어 발전과 GPU 개발로 느린 속도의 한계점을 극복하였고 합성곱 신경망으로 기존 신경망의 단점을 해소하여 이미지 인식과 음식 인식 등 다양한 곳에서 사용되고 있다. 본 논문은 합성 곱 신경망기반의 일반 이미지 인식 모델을 이용하여 흉부 X-선 영상 인식에 대해 평가하고자 하였다.
구글(Google)사에서 공개한 컴퓨터 비전(Computer Vision) 오픈 소스인 Inception v3 모델을 사용하였고 X-ray영상은 팬텀을 대상으로 흉부 부위를 4가지로 나누고 추가로 정상영상 까지 5개로 분류하여 각각 50장씩 총 250장의 영상을 학습 데이터로 분류하였고 30장을 평가데이터로 분류하였다.
학습시간은 약 10분가량 소요되었고 최종 정확도는 88.5%로 나왔다. 종합적으로 낮은 인식률을 보인 영상들은 경추뼈가 많이 나타나는 영상이 많았고 인식 오류를 나타낸 영상들은 한쪽으로 치우쳐졌거나 비스듬하게 기울어진 경우가 많았다.
컴퓨터 비전을 의료영역 적용에 대해 아직 많은 연구가 필요하며 환자들의 불규칙한 영상의 인식률을 높이기 위해 올바른 촬영법을 할 필요가 있다.
영문 초록
목차
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