Using Deep Neural Network Approach for Multiple-Class Assessment of Digital Mammography

According to the Health Promotion Administration in the Ministry of Health and Welfare statistics in Taiwan, over ten thousand women have breast cancer every year. Mammography is widely used to detect breast cancer. However, it is limited by the operator’s technique, the cooperation of the subjects,...

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Vydané v:	Healthcare (Basel) Ročník 10; číslo 12; s. 2382
Hlavní autori:	Hsu, Shih-Yen, Wang, Chi-Yuan, Kao, Yi-Kai, Liu, Kuo-Ying, Lin, Ming-Chia, Yeh, Li-Ren, Wang, Yi-Ming, Chen, Chih-I, Kao, Feng-Chen
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland MDPI AG 27.11.2022 MDPI
Predmet:	Accuracy Artificial intelligence Asymmetry Breast cancer Calcification Classification Cysts Deep learning Design Health aspects Magnetic resonance imaging Mammography Methods Mortality Neural networks Patients Radiation Technology application Tumors Ultrasonic imaging Taiwan mammography classification deep neural network
ISSN:	2227-9032, 2227-9032
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Abstract	According to the Health Promotion Administration in the Ministry of Health and Welfare statistics in Taiwan, over ten thousand women have breast cancer every year. Mammography is widely used to detect breast cancer. However, it is limited by the operator’s technique, the cooperation of the subjects, and the subjective interpretation by the physician. It results in inconsistent identification. Therefore, this study explores the use of a deep neural network algorithm for the classification of mammography images. In the experimental design, a retrospective study was used to collect imaging data from actual clinical cases. The mammography images were collected and classified according to the breast image reporting and data-analyzing system (BI-RADS). In terms of model building, a fully convolutional dense connection network (FC-DCN) is used for the network backbone. All the images were obtained through image preprocessing, a data augmentation method, and transfer learning technology to build a mammography image classification model. The research results show the model’s accuracy, sensitivity, and specificity were 86.37%, 100%, and 72.73%, respectively. Based on the FC-DCN model framework, it can effectively reduce the number of training parameters and successfully obtain a reasonable image classification model for mammography.
AbstractList	According to the Health Promotion Administration in the Ministry of Health and Welfare statistics in Taiwan, over ten thousand women have breast cancer every year. Mammography is widely used to detect breast cancer. However, it is limited by the operator’s technique, the cooperation of the subjects, and the subjective interpretation by the physician. It results in inconsistent identification. Therefore, this study explores the use of a deep neural network algorithm for the classification of mammography images. In the experimental design, a retrospective study was used to collect imaging data from actual clinical cases. The mammography images were collected and classified according to the breast image reporting and data-analyzing system (BI-RADS). In terms of model building, a fully convolutional dense connection network (FC-DCN) is used for the network backbone. All the images were obtained through image preprocessing, a data augmentation method, and transfer learning technology to build a mammography image classification model. The research results show the model’s accuracy, sensitivity, and specificity were 86.37%, 100%, and 72.73%, respectively. Based on the FC-DCN model framework, it can effectively reduce the number of training parameters and successfully obtain a reasonable image classification model for mammography. According to the Health Promotion Administration in the Ministry of Health and Welfare statistics in Taiwan, over ten thousand women have breast cancer every year. Mammography is widely used to detect breast cancer. However, it is limited by the operator's technique, the cooperation of the subjects, and the subjective interpretation by the physician. It results in inconsistent identification. Therefore, this study explores the use of a deep neural network algorithm for the classification of mammography images. In the experimental design, a retrospective study was used to collect imaging data from actual clinical cases. The mammography images were collected and classified according to the breast image reporting and data-analyzing system (BI-RADS). In terms of model building, a fully convolutional dense connection network (FC-DCN) is used for the network backbone. All the images were obtained through image preprocessing, a data augmentation method, and transfer learning technology to build a mammography image classification model. The research results show the model's accuracy, sensitivity, and specificity were 86.37%, 100%, and 72.73%, respectively. Based on the FC-DCN model framework, it can effectively reduce the number of training parameters and successfully obtain a reasonable image classification model for mammography.According to the Health Promotion Administration in the Ministry of Health and Welfare statistics in Taiwan, over ten thousand women have breast cancer every year. Mammography is widely used to detect breast cancer. However, it is limited by the operator's technique, the cooperation of the subjects, and the subjective interpretation by the physician. It results in inconsistent identification. Therefore, this study explores the use of a deep neural network algorithm for the classification of mammography images. In the experimental design, a retrospective study was used to collect imaging data from actual clinical cases. The mammography images were collected and classified according to the breast image reporting and data-analyzing system (BI-RADS). In terms of model building, a fully convolutional dense connection network (FC-DCN) is used for the network backbone. All the images were obtained through image preprocessing, a data augmentation method, and transfer learning technology to build a mammography image classification model. The research results show the model's accuracy, sensitivity, and specificity were 86.37%, 100%, and 72.73%, respectively. Based on the FC-DCN model framework, it can effectively reduce the number of training parameters and successfully obtain a reasonable image classification model for mammography.
Audience	Academic
Author	Chen, Chih-I Lin, Ming-Chia Hsu, Shih-Yen Liu, Kuo-Ying Wang, Chi-Yuan Yeh, Li-Ren Kao, Yi-Kai Wang, Yi-Ming Kao, Feng-Chen
AuthorAffiliation	13 Department of Orthopedics, E-DA Hospital, Kaohsiung City 82445, Taiwan 5 Department of Nuclear Medicine, E-DA Hospital, I-Shou University, Kaohsiung City 82445, Taiwan 7 Department of Medical Imaging and Radiology, Shu-Zen College of Medicine and Management, Kaohsiung City 82144, Taiwan 14 Department of Orthopedics, Dachang Hospital, Kaohsiung City 82445, Taiwan 8 Department of Critical Care Medicine, E-DA Hospital, I-Shou University, Kaohsiung City 82445, Taiwan 10 Division of General Medicine Surgery, Department of Surgery, E-DA Hospital, Kaohsiung City 82445, Taiwan 12 The School of Chinese Medicine for Post Baccalaureate, I-Shou University, Kaohsiung City 82445, Taiwan 6 Department of Anesthesiology, E-DA Cancer Hospital, I-Shou University, Kaohsiung City 82445, Taiwan 4 Department of Radiology, E-DA Cancer Hospital, I-Shou University, Kaohsiung City 82445, Taiwan 1 Department of Information Engineering, I-Shou University, Kaohsiung City 84001, Taiwan 11 School of Medicine, College of Med
AuthorAffiliation_xml	– name: 11 School of Medicine, College of Medicine, I-Shou University, Kaohsiung City 82445, Taiwan – name: 3 Division of Colorectal Surgery, Department of Surgery, E-DA Hospital, Kaohsiung City 82445, Taiwan – name: 9 Division of Colon and Rectal Surgery, Department of Surgery, E-DA Hospital, Kaohsiung City 82445, Taiwan – name: 4 Department of Radiology, E-DA Cancer Hospital, I-Shou University, Kaohsiung City 82445, Taiwan – name: 8 Department of Critical Care Medicine, E-DA Hospital, I-Shou University, Kaohsiung City 82445, Taiwan – name: 13 Department of Orthopedics, E-DA Hospital, Kaohsiung City 82445, Taiwan – name: 12 The School of Chinese Medicine for Post Baccalaureate, I-Shou University, Kaohsiung City 82445, Taiwan – name: 7 Department of Medical Imaging and Radiology, Shu-Zen College of Medicine and Management, Kaohsiung City 82144, Taiwan – name: 10 Division of General Medicine Surgery, Department of Surgery, E-DA Hospital, Kaohsiung City 82445, Taiwan – name: 14 Department of Orthopedics, Dachang Hospital, Kaohsiung City 82445, Taiwan – name: 2 Department of Medical Imaging and Radiological Science, I-Shou University, Kaohsiung City 82445, Taiwan – name: 6 Department of Anesthesiology, E-DA Cancer Hospital, I-Shou University, Kaohsiung City 82445, Taiwan – name: 1 Department of Information Engineering, I-Shou University, Kaohsiung City 84001, Taiwan – name: 5 Department of Nuclear Medicine, E-DA Hospital, I-Shou University, Kaohsiung City 82445, Taiwan
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SubjectTerms	Accuracy Artificial intelligence Asymmetry Breast cancer Calcification Classification Cysts Deep learning Design Health aspects Magnetic resonance imaging Mammography Methods Mortality Neural networks Patients Radiation Technology application Tumors Ultrasonic imaging
Title	Using Deep Neural Network Approach for Multiple-Class Assessment of Digital Mammography
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