Breast Tumor Detection and Diagnosis Using an Improved Faster R-CNN in DCE-MRI
AI-based breast cancer detection can improve the sensitivity and specificity of detection, especially for small lesions, which has clinical value in realizing early detection and treatment so as to reduce mortality. The two-stage detection network performs well; however, it adopts an imprecise ROI d...
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| Format: | Article |
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MDPI AG
2024-12-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/11/12/1217 |
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| author | Haitian Gui Han Jiao Li Li Xinhua Jiang Tao Su Zhiyong Pang |
| author_facet | Haitian Gui Han Jiao Li Li Xinhua Jiang Tao Su Zhiyong Pang |
| author_sort | Haitian Gui |
| collection | DOAJ |
| description | AI-based breast cancer detection can improve the sensitivity and specificity of detection, especially for small lesions, which has clinical value in realizing early detection and treatment so as to reduce mortality. The two-stage detection network performs well; however, it adopts an imprecise ROI during classification, which can easily include surrounding tumor tissues. Additionally, fuzzy noise is a significant contributor to false positives. We adopted Faster RCNN as the architecture, introduced ROI aligning to minimize quantization errors and feature pyramid network (FPN) to extract different resolution features, added a bounding box quadratic regression feature map extraction network and three convolutional layers to reduce interference from tumor surrounding information, and extracted more accurate and deeper feature maps. Our approach outperformed Faster R-CNN, Mask R-CNN, and YOLOv9 in breast cancer detection across 485 internal cases. We achieved superior performance in mAP, sensitivity, and false positive rate ((0.752, 0.950, 0.133) vs. (0.711, 0.950, 0.200) vs. (0.718, 0.880, 0.120) vs. (0.658, 0.680, 405)), which represents a 38.5% reduction in false positives compared to manual detection. Additionally, in a public dataset of 220 cases, our model also demonstrated the best performance. It showed improved sensitivity and specificity, effectively assisting doctors in diagnosing cancer. |
| format | Article |
| id | doaj-art-bad0937d58c94130a3f12943ccb96cc9 |
| institution | Kabale University |
| issn | 2306-5354 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj-art-bad0937d58c94130a3f12943ccb96cc92024-12-27T14:11:31ZengMDPI AGBioengineering2306-53542024-12-011112121710.3390/bioengineering11121217Breast Tumor Detection and Diagnosis Using an Improved Faster R-CNN in DCE-MRIHaitian Gui0Han Jiao1Li Li2Xinhua Jiang3Tao Su4Zhiyong Pang5School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, ChinaSchool of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, ChinaState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Department of Medical Imaging, Sun Yat-sen University Cancer Center (SYSUCC), Guangzhou 510060, ChinaState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Department of Medical Imaging, Sun Yat-sen University Cancer Center (SYSUCC), Guangzhou 510060, ChinaSchool of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, ChinaSchool of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, ChinaAI-based breast cancer detection can improve the sensitivity and specificity of detection, especially for small lesions, which has clinical value in realizing early detection and treatment so as to reduce mortality. The two-stage detection network performs well; however, it adopts an imprecise ROI during classification, which can easily include surrounding tumor tissues. Additionally, fuzzy noise is a significant contributor to false positives. We adopted Faster RCNN as the architecture, introduced ROI aligning to minimize quantization errors and feature pyramid network (FPN) to extract different resolution features, added a bounding box quadratic regression feature map extraction network and three convolutional layers to reduce interference from tumor surrounding information, and extracted more accurate and deeper feature maps. Our approach outperformed Faster R-CNN, Mask R-CNN, and YOLOv9 in breast cancer detection across 485 internal cases. We achieved superior performance in mAP, sensitivity, and false positive rate ((0.752, 0.950, 0.133) vs. (0.711, 0.950, 0.200) vs. (0.718, 0.880, 0.120) vs. (0.658, 0.680, 405)), which represents a 38.5% reduction in false positives compared to manual detection. Additionally, in a public dataset of 220 cases, our model also demonstrated the best performance. It showed improved sensitivity and specificity, effectively assisting doctors in diagnosing cancer.https://www.mdpi.com/2306-5354/11/12/1217breast cancer detectiondeep learningAI assistant |
| spellingShingle | Haitian Gui Han Jiao Li Li Xinhua Jiang Tao Su Zhiyong Pang Breast Tumor Detection and Diagnosis Using an Improved Faster R-CNN in DCE-MRI Bioengineering breast cancer detection deep learning AI assistant |
| title | Breast Tumor Detection and Diagnosis Using an Improved Faster R-CNN in DCE-MRI |
| title_full | Breast Tumor Detection and Diagnosis Using an Improved Faster R-CNN in DCE-MRI |
| title_fullStr | Breast Tumor Detection and Diagnosis Using an Improved Faster R-CNN in DCE-MRI |
| title_full_unstemmed | Breast Tumor Detection and Diagnosis Using an Improved Faster R-CNN in DCE-MRI |
| title_short | Breast Tumor Detection and Diagnosis Using an Improved Faster R-CNN in DCE-MRI |
| title_sort | breast tumor detection and diagnosis using an improved faster r cnn in dce mri |
| topic | breast cancer detection deep learning AI assistant |
| url | https://www.mdpi.com/2306-5354/11/12/1217 |
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