Application of Deep Learning to Construct Breast Cancer Diagnosis Model
(1) Background: According to Taiwan’s ministry of health statistics, the rate of breast cancer in women is increasing annually. Each year, more than 10,000 women suffer from breast cancer, and over 2000 die of the disease. The mortality rate is annually increasing, but if breast cancer tumors are de...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/4/1957 |
| _version_ | 1797483046153224192 |
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| author | Rong-Ho Lin Benjamin Kofi Kujabi Chun-Ling Chuang Ching-Shun Lin Chun-Jen Chiu |
| author_facet | Rong-Ho Lin Benjamin Kofi Kujabi Chun-Ling Chuang Ching-Shun Lin Chun-Jen Chiu |
| author_sort | Rong-Ho Lin |
| collection | DOAJ |
| description | (1) Background: According to Taiwan’s ministry of health statistics, the rate of breast cancer in women is increasing annually. Each year, more than 10,000 women suffer from breast cancer, and over 2000 die of the disease. The mortality rate is annually increasing, but if breast cancer tumors are detected earlier, and appropriate treatment is provided immediately, the survival rate of patients will increase enormously. (2) Methods: This research aimed to develop a stepwise breast cancer model architecture to improve diagnostic accuracy and reduce the misdiagnosis rate of breast cancer. In the first stage, a breast cancer risk factor dataset was utilized. After pre-processing, Artificial Neural Network (ANN) and the support vector machine (SVM) were applied to the dataset to classify breast cancer tumors and compare their performances. The ANN achieved 76.6% classification accuracy, and the SVM using radial functions achieved the best classification accuracy of 91.6%. Therefore, SVM was utilized in the determination of results concerning the relevant breast cancer risk factors. In the second stage, we trained AlexNet, ResNet101, and InceptionV3 networks using transfer learning. The networks were studied using Adaptive Moment Estimation (ADAM) and Stochastic Gradient Descent with Momentum (SGDM) based optimization algorithm to diagnose benign and malignant tumors, and the results were evaluated; (3) Results: According to the results, AlexNet obtained 81.16%, ResNet101 85.51%, and InceptionV3 achieved a remarkable accuracy of 91.3%. The results of the three models were utilized in establishing a voting combination, and the soft-voting method was applied to average the prediction result for which a test accuracy of 94.20% was obtained; (4) Conclusions: Despite the small number of images in this study, the accuracy is higher compared to other literature. The proposed method has demonstrated the need for an additional productive tool in clinical settings when radiologists are evaluating mammography images of patients. |
| first_indexed | 2024-03-09T22:41:17Z |
| format | Article |
| id | doaj.art-1541330c66204d4cbe849b30457cfe2e |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T22:41:17Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-1541330c66204d4cbe849b30457cfe2e2023-11-23T18:36:53ZengMDPI AGApplied Sciences2076-34172022-02-01124195710.3390/app12041957Application of Deep Learning to Construct Breast Cancer Diagnosis ModelRong-Ho Lin0Benjamin Kofi Kujabi1Chun-Ling Chuang2Ching-Shun Lin3Chun-Jen Chiu4Department of Industrial Engineering and Management, National Taipei University of Technology, 1, Sec. 3, Zhongxiao E. Rd., Taipei 10608, TaiwanDepartment of Industrial Engineering and Management, National Taipei University of Technology, 1, Sec. 3, Zhongxiao E. Rd., Taipei 10608, TaiwanDepartment of Information Management, Kainan University, Taoyuan City 33857, TaiwanDepartment of Industrial Engineering and Management, National Taipei University of Technology, 1, Sec. 3, Zhongxiao E. Rd., Taipei 10608, TaiwanDepartment of Industrial Engineering and Management, National Taipei University of Technology, 1, Sec. 3, Zhongxiao E. Rd., Taipei 10608, Taiwan(1) Background: According to Taiwan’s ministry of health statistics, the rate of breast cancer in women is increasing annually. Each year, more than 10,000 women suffer from breast cancer, and over 2000 die of the disease. The mortality rate is annually increasing, but if breast cancer tumors are detected earlier, and appropriate treatment is provided immediately, the survival rate of patients will increase enormously. (2) Methods: This research aimed to develop a stepwise breast cancer model architecture to improve diagnostic accuracy and reduce the misdiagnosis rate of breast cancer. In the first stage, a breast cancer risk factor dataset was utilized. After pre-processing, Artificial Neural Network (ANN) and the support vector machine (SVM) were applied to the dataset to classify breast cancer tumors and compare their performances. The ANN achieved 76.6% classification accuracy, and the SVM using radial functions achieved the best classification accuracy of 91.6%. Therefore, SVM was utilized in the determination of results concerning the relevant breast cancer risk factors. In the second stage, we trained AlexNet, ResNet101, and InceptionV3 networks using transfer learning. The networks were studied using Adaptive Moment Estimation (ADAM) and Stochastic Gradient Descent with Momentum (SGDM) based optimization algorithm to diagnose benign and malignant tumors, and the results were evaluated; (3) Results: According to the results, AlexNet obtained 81.16%, ResNet101 85.51%, and InceptionV3 achieved a remarkable accuracy of 91.3%. The results of the three models were utilized in establishing a voting combination, and the soft-voting method was applied to average the prediction result for which a test accuracy of 94.20% was obtained; (4) Conclusions: Despite the small number of images in this study, the accuracy is higher compared to other literature. The proposed method has demonstrated the need for an additional productive tool in clinical settings when radiologists are evaluating mammography images of patients.https://www.mdpi.com/2076-3417/12/4/1957breast cancerneural networksupport vector machinedeep learningconvolutional neural network |
| spellingShingle | Rong-Ho Lin Benjamin Kofi Kujabi Chun-Ling Chuang Ching-Shun Lin Chun-Jen Chiu Application of Deep Learning to Construct Breast Cancer Diagnosis Model Applied Sciences breast cancer neural network support vector machine deep learning convolutional neural network |
| title | Application of Deep Learning to Construct Breast Cancer Diagnosis Model |
| title_full | Application of Deep Learning to Construct Breast Cancer Diagnosis Model |
| title_fullStr | Application of Deep Learning to Construct Breast Cancer Diagnosis Model |
| title_full_unstemmed | Application of Deep Learning to Construct Breast Cancer Diagnosis Model |
| title_short | Application of Deep Learning to Construct Breast Cancer Diagnosis Model |
| title_sort | application of deep learning to construct breast cancer diagnosis model |
| topic | breast cancer neural network support vector machine deep learning convolutional neural network |
| url | https://www.mdpi.com/2076-3417/12/4/1957 |
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