합성곱신경망의 학습 및 테스트자료에 따른 골다공증 판독에 미치는 영향
Effect of Training and Testing Condition of Convolutional Neural Network on evaluating Osteoporosis
김재윤, 이재서, 강병철, 김형석, Shyam Adhikan, Liu Liu, 윤숙자,
소속 상세정보
김재윤 ( Kim jae-Yun ) - 전남대학교 치의학전문대학원
이재서 ( Lee Jae-Seo ) - 전남대학교 치의학전문대학원 구강악안면방사선학교실
강병철 ( Kang Byung-Cheol ) - 전남대학교 치의학전문대학원 구강악안면방사선학교실
김형석 ( Kim Hyong-Suk ) - 전남대학교 전자공학부
( Shyam Adhikan ) - 전남대학교 전자공학부
( Liu Liu ) - 전남대학교 전자공학부
윤숙자 ( Yoon Suk-Ja ) - 전남대학교 치의학전문대학원 구강악안면방사선학교실
Abstract
This study aimed to test a convolutional neural network (CNN) in two different settings of training and testing data. Panoramic radiographs were selected from 1170 female dental patients (mean age 49.19 ± 21.91 yr). The cortical bone of the mandible inferior border was evaluated for osteoporosis or normal condition on the panoramic radiographs. Among them, 586 patients (mean age 27.46 ± 6.73 yr) had normal condition, and osteoporosis was interpreted on 584 patients (mean age 71.00 ± 7.64 yr). Among them, one data set of 569 normal patients (mean age 26.61 ± 4.60 yr) and 502 osteoporosis patients (mean age 72.37 ± 7.10 yr) was used for training CNN, and the other data set of 17 normal patients (mean age 55.94 ± 4.0 yr) and 82 osteoporosis patients (mean age 62.60 ± 5.00 yr) for testing CNN in the first experiment, while the latter was used for training CNN and the former for testing CNN in the second experiment.
The error rate was 15.15% in the first experiment and 5.14% in the second experiment. This study suggests that age-matched training data make more accurate testing results.
키워드
Mandible; Osteoporosis; Panoramic radiograph; Computer
원문 및 링크아웃 정보
등재저널 정보