A High-Performance Deep Neural Network Model for BI-RADS Classification of Screening Mammography
Globally, the incidence rate for breast cancer ranks first. Treatment for early-stage breast cancer is highly cost effective. Five-year survival rate for stage 0–2 breast cancer exceeds 90%. Screening mammography has been acknowledged as the most reliable way to diagnose breast cancer at an early st...
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MDPI AG
2022-02-01
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| Online Access: | https://www.mdpi.com/1424-8220/22/3/1160 |
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| author | Kuen-Jang Tsai Mei-Chun Chou Hao-Ming Li Shin-Tso Liu Jung-Hsiu Hsu Wei-Cheng Yeh Chao-Ming Hung Cheng-Yu Yeh Shaw-Hwa Hwang |
| author_facet | Kuen-Jang Tsai Mei-Chun Chou Hao-Ming Li Shin-Tso Liu Jung-Hsiu Hsu Wei-Cheng Yeh Chao-Ming Hung Cheng-Yu Yeh Shaw-Hwa Hwang |
| author_sort | Kuen-Jang Tsai |
| collection | DOAJ |
| description | Globally, the incidence rate for breast cancer ranks first. Treatment for early-stage breast cancer is highly cost effective. Five-year survival rate for stage 0–2 breast cancer exceeds 90%. Screening mammography has been acknowledged as the most reliable way to diagnose breast cancer at an early stage. Taiwan government has been urging women without any symptoms, aged between 45 and 69, to have a screening mammogram bi-yearly. This brings about a large workload for radiologists. In light of this, this paper presents a deep neural network (DNN)-based model as an efficient and reliable tool to assist radiologists with mammographic interpretation. For the first time in the literature, mammograms are completely classified into BI-RADS categories 0, 1, 2, 3, 4A, 4B, 4C and 5. The proposed model was trained using block-based images segmented from a mammogram dataset of our own. A block-based image was applied to the model as an input, and a BI-RADS category was predicted as an output. At the end of this paper, the outperformance of this work is demonstrated by an overall accuracy of 94.22%, an average sensitivity of 95.31%, an average specificity of 99.15% and an area under curve (AUC) of 0.9723. When applied to breast cancer screening for Asian women who are more likely to have dense breasts, this model is expected to give a higher accuracy than others in the literature, since it was trained using mammograms taken from Taiwanese women. |
| first_indexed | 2024-03-09T23:06:41Z |
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| institution | Directory Open Access Journal |
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| language | English |
| last_indexed | 2024-03-09T23:06:41Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3289daa0b6f74e58b8f294e29e44da152023-11-23T17:51:25ZengMDPI AGSensors1424-82202022-02-01223116010.3390/s22031160A High-Performance Deep Neural Network Model for BI-RADS Classification of Screening MammographyKuen-Jang Tsai0Mei-Chun Chou1Hao-Ming Li2Shin-Tso Liu3Jung-Hsiu Hsu4Wei-Cheng Yeh5Chao-Ming Hung6Cheng-Yu Yeh7Shaw-Hwa Hwang8Department of General Surgey, E-Da Cancer Hospital, Yanchao Dist., Kaohsiung 82445, TaiwanDepartment of Radiology, E-Da Hospital, Yanchao Dist., Kaohsiung 82445, TaiwanDepartment of Radiology, E-Da Hospital, Yanchao Dist., Kaohsiung 82445, TaiwanDepartment of Radiology, E-Da Hospital, Yanchao Dist., Kaohsiung 82445, TaiwanDepartment of Radiology, E-Da Hospital, Yanchao Dist., Kaohsiung 82445, TaiwanDepartment of Radiology, E-Da Cancer Hospital, Yanchao Dist., Kaohsiung 82445, TaiwanDepartment of General Surgey, E-Da Cancer Hospital, Yanchao Dist., Kaohsiung 82445, TaiwanDepartment of Electrical Engineering, National Chin-Yi University of Technology, Taichung 41170, TaiwanDepartment of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, TaiwanGlobally, the incidence rate for breast cancer ranks first. Treatment for early-stage breast cancer is highly cost effective. Five-year survival rate for stage 0–2 breast cancer exceeds 90%. Screening mammography has been acknowledged as the most reliable way to diagnose breast cancer at an early stage. Taiwan government has been urging women without any symptoms, aged between 45 and 69, to have a screening mammogram bi-yearly. This brings about a large workload for radiologists. In light of this, this paper presents a deep neural network (DNN)-based model as an efficient and reliable tool to assist radiologists with mammographic interpretation. For the first time in the literature, mammograms are completely classified into BI-RADS categories 0, 1, 2, 3, 4A, 4B, 4C and 5. The proposed model was trained using block-based images segmented from a mammogram dataset of our own. A block-based image was applied to the model as an input, and a BI-RADS category was predicted as an output. At the end of this paper, the outperformance of this work is demonstrated by an overall accuracy of 94.22%, an average sensitivity of 95.31%, an average specificity of 99.15% and an area under curve (AUC) of 0.9723. When applied to breast cancer screening for Asian women who are more likely to have dense breasts, this model is expected to give a higher accuracy than others in the literature, since it was trained using mammograms taken from Taiwanese women.https://www.mdpi.com/1424-8220/22/3/1160screening mammographybreast imaging reporting and data system (BI-RADS)image classificationdeep neural network (DNN)deep learning |
| spellingShingle | Kuen-Jang Tsai Mei-Chun Chou Hao-Ming Li Shin-Tso Liu Jung-Hsiu Hsu Wei-Cheng Yeh Chao-Ming Hung Cheng-Yu Yeh Shaw-Hwa Hwang A High-Performance Deep Neural Network Model for BI-RADS Classification of Screening Mammography Sensors screening mammography breast imaging reporting and data system (BI-RADS) image classification deep neural network (DNN) deep learning |
| title | A High-Performance Deep Neural Network Model for BI-RADS Classification of Screening Mammography |
| title_full | A High-Performance Deep Neural Network Model for BI-RADS Classification of Screening Mammography |
| title_fullStr | A High-Performance Deep Neural Network Model for BI-RADS Classification of Screening Mammography |
| title_full_unstemmed | A High-Performance Deep Neural Network Model for BI-RADS Classification of Screening Mammography |
| title_short | A High-Performance Deep Neural Network Model for BI-RADS Classification of Screening Mammography |
| title_sort | high performance deep neural network model for bi rads classification of screening mammography |
| topic | screening mammography breast imaging reporting and data system (BI-RADS) image classification deep neural network (DNN) deep learning |
| url | https://www.mdpi.com/1424-8220/22/3/1160 |
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