Detection and classification of breast cancer in mammographic images with fine-tuned convolutional neural networks
Breast cancer is cancer that forms in the cells of the breasts and is a severe health issue that affects many people around the world, especially since it is the most deadly cancer in women. By finding it early and using new treatments, patients can overcome this challenge and get back to a healthie...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-04-01
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| Series: | Journal of Information and Telecommunication |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/24751839.2024.2415033 |
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| author | Huong Hoang Luong Hai Thanh Nguyen Nguyen Thai-Nghe |
| author_facet | Huong Hoang Luong Hai Thanh Nguyen Nguyen Thai-Nghe |
| author_sort | Huong Hoang Luong |
| collection | DOAJ |
| description | Breast cancer is cancer that forms in the cells of the breasts and is a severe health issue that affects many people around the world, especially since it is the most deadly cancer in women. By finding it early and using new treatments, patients can overcome this challenge and get back to a healthier life. This study proposed a procedure to fine-tune the Convolutional Neural Networks (CNN) model with data preprocessing and augmentation in classifying mammogram images called the Hybrid Mammogram Classification and Detection Pipeline (HMCaD). After using CNN for classification because it brings higher confidence in classifying tasks, the YOLOv8 has been applied for localization subtask to detect abnormal positions with predicted bounding boxes. The database is provided by the Mammographic Image Analysis Society (MIAS) and is protected by the United Kingdom. It comprises 330 samples, including 79 benign, 54 malignant, and 207 normal images. As a result, the classification in our model based on the custom EfficientNetB3 model and seam carving technique received a great validation accuracy, test accuracy, and F1 score throughout three scenarios at 96.73%, 97.59%, and 97.58%, respectively. Furthermore, the area under the Receiver Operating Characteristic (ROC) curve also has a surprise result of 0.96 (i.e. [Formula: see text]). Moreover, YOLOv8 for detecting abnormal positions in our study achieved 83.22% in Intersection over Union (IoU). This led to the research reaching good results in classifying and detecting breast cancer by considering several performance metrics. |
| format | Article |
| id | doaj-art-95ea48cb40a3485c862d1d435c4e4ff8 |
| institution | OA Journals |
| issn | 2475-1839 2475-1847 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Information and Telecommunication |
| spelling | doaj-art-95ea48cb40a3485c862d1d435c4e4ff82025-08-20T02:33:11ZengTaylor & Francis GroupJournal of Information and Telecommunication2475-18392475-18472025-04-019220923610.1080/24751839.2024.2415033Detection and classification of breast cancer in mammographic images with fine-tuned convolutional neural networksHuong Hoang Luong0Hai Thanh Nguyen1Nguyen Thai-Nghe2College of ICT, Can Tho University, Can Tho, VietnamCollege of ICT, Can Tho University, Can Tho, VietnamCollege of ICT, Can Tho University, Can Tho, VietnamBreast cancer is cancer that forms in the cells of the breasts and is a severe health issue that affects many people around the world, especially since it is the most deadly cancer in women. By finding it early and using new treatments, patients can overcome this challenge and get back to a healthier life. This study proposed a procedure to fine-tune the Convolutional Neural Networks (CNN) model with data preprocessing and augmentation in classifying mammogram images called the Hybrid Mammogram Classification and Detection Pipeline (HMCaD). After using CNN for classification because it brings higher confidence in classifying tasks, the YOLOv8 has been applied for localization subtask to detect abnormal positions with predicted bounding boxes. The database is provided by the Mammographic Image Analysis Society (MIAS) and is protected by the United Kingdom. It comprises 330 samples, including 79 benign, 54 malignant, and 207 normal images. As a result, the classification in our model based on the custom EfficientNetB3 model and seam carving technique received a great validation accuracy, test accuracy, and F1 score throughout three scenarios at 96.73%, 97.59%, and 97.58%, respectively. Furthermore, the area under the Receiver Operating Characteristic (ROC) curve also has a surprise result of 0.96 (i.e. [Formula: see text]). Moreover, YOLOv8 for detecting abnormal positions in our study achieved 83.22% in Intersection over Union (IoU). This led to the research reaching good results in classifying and detecting breast cancer by considering several performance metrics.https://www.tandfonline.com/doi/10.1080/24751839.2024.2415033Breast cancer predictionTransfer learningYolov8EfficientNetB3 |
| spellingShingle | Huong Hoang Luong Hai Thanh Nguyen Nguyen Thai-Nghe Detection and classification of breast cancer in mammographic images with fine-tuned convolutional neural networks Journal of Information and Telecommunication Breast cancer prediction Transfer learning Yolov8 EfficientNetB3 |
| title | Detection and classification of breast cancer in mammographic images with fine-tuned convolutional neural networks |
| title_full | Detection and classification of breast cancer in mammographic images with fine-tuned convolutional neural networks |
| title_fullStr | Detection and classification of breast cancer in mammographic images with fine-tuned convolutional neural networks |
| title_full_unstemmed | Detection and classification of breast cancer in mammographic images with fine-tuned convolutional neural networks |
| title_short | Detection and classification of breast cancer in mammographic images with fine-tuned convolutional neural networks |
| title_sort | detection and classification of breast cancer in mammographic images with fine tuned convolutional neural networks |
| topic | Breast cancer prediction Transfer learning Yolov8 EfficientNetB3 |
| url | https://www.tandfonline.com/doi/10.1080/24751839.2024.2415033 |
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