An Approach for Breast Cancer X-Ray Images Classification Based on Vision Transformer
Today, due to the problem of environmental pollution, water, and other factors have caused many dangerous diseases, including cancer. According to recent statistics, breast cancer is one of the leading diseases in women, and this disease tends to increase more and more. To detect and diagnose the di...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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World Scientific Publishing
2025-08-01
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| Series: | Vietnam Journal of Computer Science |
| Subjects: | |
| Online Access: | https://www.worldscientific.com/doi/10.1142/S2196888824500210 |
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| _version_ | 1849339716122443776 |
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| author | Huong Hoang Luong Kiet Tuan Pham Dat Thanh Le Danh Le Pham Thanh Long Le Hoang Hai Hoang Nhat Nguyen Nguyen Thai-Nghe Hai Thanh Nguyen |
| author_facet | Huong Hoang Luong Kiet Tuan Pham Dat Thanh Le Danh Le Pham Thanh Long Le Hoang Hai Hoang Nhat Nguyen Nguyen Thai-Nghe Hai Thanh Nguyen |
| author_sort | Huong Hoang Luong |
| collection | DOAJ |
| description | Today, due to the problem of environmental pollution, water, and other factors have caused many dangerous diseases, including cancer. According to recent statistics, breast cancer is one of the leading diseases in women, and this disease tends to increase more and more. To detect and diagnose the disease, doctors perform many examinations: self-examination, clinical examination, X-ray, ultrasound screening, etc., in which X-ray is a highly effective method. This study proposes an approach to detecting and classifying breast cancer on an X-ray dataset using a refined Vision Transformer (ViT), ViT-B32. The considered dataset contains about 7000 X-ray images from patients aged 27 to 90, labeled as malignant, benign, or normal. As presented in scenarios, the study yielded positive results, with 91% to 94% in ACC and F1-score metrics. Furthermore, it has shown that the results obtained for breast cancer detection on X-ray images using the fine-tuned ViT architecture outperformed CNN models such as VGG16, MobileNet, Xception, ResNet50, and some state-of-the-art approaches. |
| format | Article |
| id | doaj-art-85cca240dddf4c2bb4e6baf0b4875f61 |
| institution | Kabale University |
| issn | 2196-8888 2196-8896 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | World Scientific Publishing |
| record_format | Article |
| series | Vietnam Journal of Computer Science |
| spelling | doaj-art-85cca240dddf4c2bb4e6baf0b4875f612025-08-20T03:44:04ZengWorld Scientific PublishingVietnam Journal of Computer Science2196-88882196-88962025-08-01120332934710.1142/S2196888824500210An Approach for Breast Cancer X-Ray Images Classification Based on Vision TransformerHuong Hoang Luong0Kiet Tuan Pham1Dat Thanh Le2Danh Le Pham Thanh3Long Le Hoang Hai4Hoang Nhat Nguyen5Nguyen Thai-Nghe6Hai Thanh Nguyen7Can Tho University, Can Tho City, VietnamFPT University, Can Tho City, VietnamFPT University, Can Tho City, VietnamFPT University, Can Tho City, VietnamFPT University, Can Tho City, VietnamFPT University, Can Tho City, VietnamCollege of Information and Communication Technology, Can Tho University, Can Tho City, VietnamCollege of Information and Communication Technology, Can Tho University, Can Tho City, VietnamToday, due to the problem of environmental pollution, water, and other factors have caused many dangerous diseases, including cancer. According to recent statistics, breast cancer is one of the leading diseases in women, and this disease tends to increase more and more. To detect and diagnose the disease, doctors perform many examinations: self-examination, clinical examination, X-ray, ultrasound screening, etc., in which X-ray is a highly effective method. This study proposes an approach to detecting and classifying breast cancer on an X-ray dataset using a refined Vision Transformer (ViT), ViT-B32. The considered dataset contains about 7000 X-ray images from patients aged 27 to 90, labeled as malignant, benign, or normal. As presented in scenarios, the study yielded positive results, with 91% to 94% in ACC and F1-score metrics. Furthermore, it has shown that the results obtained for breast cancer detection on X-ray images using the fine-tuned ViT architecture outperformed CNN models such as VGG16, MobileNet, Xception, ResNet50, and some state-of-the-art approaches.https://www.worldscientific.com/doi/10.1142/S2196888824500210Breast cancerX-ray images classificationVision Transformerdeep learning |
| spellingShingle | Huong Hoang Luong Kiet Tuan Pham Dat Thanh Le Danh Le Pham Thanh Long Le Hoang Hai Hoang Nhat Nguyen Nguyen Thai-Nghe Hai Thanh Nguyen An Approach for Breast Cancer X-Ray Images Classification Based on Vision Transformer Vietnam Journal of Computer Science Breast cancer X-ray images classification Vision Transformer deep learning |
| title | An Approach for Breast Cancer X-Ray Images Classification Based on Vision Transformer |
| title_full | An Approach for Breast Cancer X-Ray Images Classification Based on Vision Transformer |
| title_fullStr | An Approach for Breast Cancer X-Ray Images Classification Based on Vision Transformer |
| title_full_unstemmed | An Approach for Breast Cancer X-Ray Images Classification Based on Vision Transformer |
| title_short | An Approach for Breast Cancer X-Ray Images Classification Based on Vision Transformer |
| title_sort | approach for breast cancer x ray images classification based on vision transformer |
| topic | Breast cancer X-ray images classification Vision Transformer deep learning |
| url | https://www.worldscientific.com/doi/10.1142/S2196888824500210 |
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