Takeshi Takaki1, Seiichi Murakami2, Ryo Watanabe3, Takatoshi Aoki4, Toshioh Fujibuchi5. 1. Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan; Department of Radiology, Hospital of University of Occupational and Environmental Health, Iseigaoka 1-1, Yahatanishi-ku, Kitakyushu-shi, Fukuoka 807-8555, Japan. Electronic address: takaki-t@clnc.uoeh-u.ac.jp. 2. Department of Radiological Science, Faculty of Health Sciences, Junshin Gakuen University, 1-1-1 Chikushigaoka, Minami-ku, Fukuoka 815-8510, Japan. 3. Department of Radiology, Hospital of University of Occupational and Environmental Health, Iseigaoka 1-1, Yahatanishi-ku, Kitakyushu-shi, Fukuoka 807-8555, Japan. 4. Department of Radiology, University of Occupational and Environmental Health School of Medicine, Iseigaoka 1-1, Yahatanishi-ku, Kitakyushu-shi, Fukuoka 807-8555, Japan. 5. Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan.
Abstract
PURPOSE: The objective of this study is to determine the quality of chest X-ray images using a deep convolutional neural network (DCNN) and a rule base without performing any visual assessment. A method is proposed for determining the minimum diagnosable exposure index (EI) and the target exposure index (EIt). METHODS: The proposed method involves transfer learning to assess the lung fields, mediastinum, and spine using GoogLeNet, which is a type of DCNN that has been trained using conventional images. Three detectors were created, and the image quality of local regions was rated. Subsequently, the results were used to determine the overall quality of chest X-ray images using a rule-based technique that was in turn based on expert assessment. The minimum EI required for diagnosis was calculated based on the distribution of the EI values, which were classified as either suitable or non-suitable and then used to ascertain the EIt. RESULTS: The accuracy rate using the DCNN and the rule base was 81%. The minimum EI required for diagnosis was 230, and the EIt was 288. CONCLUSION: The results indicated that the proposed method using the DCNN and the rule base could discriminate different image qualities without any visual assessment; moreover, it could determine both the minimum EI required for diagnosis and the EIt.
PURPOSE: The objective of this study is to determine the quality of chest X-ray images using a deep convolutional neural network (DCNN) and a rule base without performing any visual assessment. A method is proposed for determining the minimum diagnosable exposure index (EI) and the target exposure index (EIt). METHODS: The proposed method involves transfer learning to assess the lung fields, mediastinum, and spine using GoogLeNet, which is a type of DCNN that has been trained using conventional images. Three detectors were created, and the image quality of local regions was rated. Subsequently, the results were used to determine the overall quality of chest X-ray images using a rule-based technique that was in turn based on expert assessment. The minimum EI required for diagnosis was calculated based on the distribution of the EI values, which were classified as either suitable or non-suitable and then used to ascertain the EIt. RESULTS: The accuracy rate using the DCNN and the rule base was 81%. The minimum EI required for diagnosis was 230, and the EIt was 288. CONCLUSION: The results indicated that the proposed method using the DCNN and the rule base could discriminate different image qualities without any visual assessment; moreover, it could determine both the minimum EI required for diagnosis and the EIt.