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흉부 방사선영상의 좌, 우 반전 발생 여부 컨벌루션 신경망 기반 정확도 평가

An Accuracy Evaluation on Convolutional Neural Network Assessment of Orientation Reversal of Chest X-ray Image

방사선기술과학 2020년 43권 2호 p.65 ~ 70
이현우 ( Lee Hyun-Woo ) - Shingu College Department of Radiological Technology

오주영 ( Oh Joo-Young ) - Chungbuk National University Graduate School Department of Biomedical Engineering
이주영 ( Lee Joo-Young ) - Songho University Department of Radiological Technology
이태수 ( Lee Tae-Soo ) - Chungbuk National University Graduate School Department of Biomedical Engineering
박훈희 ( Park Hoon-Hee ) - Shingu College Department of Radiological Technology

Abstract


PA(postero-anterior) and AP(antero-posterior) chest projections are the most sought-after types of all kinds of projections. But if a radiological technologist puts wrong information about the position in the computer, the orientation of left and right side of an image would be reversed. In order to solve this problem, we utilized CNN(convolutional neural network) which has recently utilized a lot for studies of medical imaging technology and rule-based system. 70% of 111,622 chest images were used for training, 20% of them were used for testing and 10% of them were used for validation set in the CNN experiment. The same amount of images which were used for testing in the CNN experiment were used in rule-based system. Python 3.7 version and Tensorflow r1.14 were utilized for data environment. As a result, rule-based system had 66% accuracy on evaluating whether the orientation reversal on chest x-ray image. But the CNN had 97.9% accuracy on that. Being overcome limitations by CNN which had been shown on rule-based system and shown the high accuracy can be considered as a meaningful result. If some problems which can occur for tasks of the radiological technologist can be separated by utilizing CNN, It can contribute a lot to optimize workflow.

키워드

흉부검사 ; 방사선영상 ; 방향반전 ; 규칙기반 시스템 ; 컨벌루션 신경망
Chest projection ; X-ray image ; Orientation reversal ; Rule-based system ; Convolutional neural network
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