FastLeakyResNet-CIR: A Novel Deep Learning Framework for Breast Cancer Detection and Classification
Breast cancer is a type of disease that primarily affects the breast tissue, and it is crucial to achieve early diagnosis for successful treatment and recovery. In recent years, the residual network (ResNet) has gained significant attention in the detection of breast cancer using medical images. In...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10531258/ |
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| _version_ | 1846170453936177152 |
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| author | Ruiming Zeng Boan Qu Wei Liu Jianghao Li Hongshen Li Pingping Bing Shuangni Duan Lemei Zhu |
| author_facet | Ruiming Zeng Boan Qu Wei Liu Jianghao Li Hongshen Li Pingping Bing Shuangni Duan Lemei Zhu |
| author_sort | Ruiming Zeng |
| collection | DOAJ |
| description | Breast cancer is a type of disease that primarily affects the breast tissue, and it is crucial to achieve early diagnosis for successful treatment and recovery. In recent years, the residual network (ResNet) has gained significant attention in the detection of breast cancer using medical images. In this paper, we propose an efficient and robust deep learning framework called FastLeakyResNet-CIR, an improved ResNet architecture, for breast cancer detection and classification. The FastLeakyResNet-CIR achieves an impressive accuracy of 98.94% when evaluated on a dataset of 7909 microscopic images of breast tumor tissue from BreakHis dataset, which outperforms the state-of-the-art methods, e.g. ResNet18, ResNet50, InceptionV3 and VGG16. The experiment results further highlight the potential of FastLeakyResNet-CIR for accurate and rapid diagnosis of breast cancer, thus facilitating effective medical treatment for patients. |
| format | Article |
| id | doaj-art-9ce9961a8cfb49e0b70b78c7020154c4 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-9ce9961a8cfb49e0b70b78c7020154c42024-11-12T00:01:01ZengIEEEIEEE Access2169-35362024-01-0112708257083210.1109/ACCESS.2024.340172910531258FastLeakyResNet-CIR: A Novel Deep Learning Framework for Breast Cancer Detection and ClassificationRuiming Zeng0Boan Qu1Wei Liu2https://orcid.org/0000-0003-2702-0362Jianghao Li3Hongshen Li4Pingping Bing5https://orcid.org/0000-0001-6956-2819Shuangni Duan6Lemei Zhu7Hunan Provincial Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha, ChinaCollege of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing, ChinaCollege of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing, ChinaHunan Provincial Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha, ChinaHunan Provincial Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha, ChinaHunan Provincial Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha, ChinaSchool of Public Health, Xiangnan University, Chenzhou, ChinaHunan Provincial Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha, ChinaBreast cancer is a type of disease that primarily affects the breast tissue, and it is crucial to achieve early diagnosis for successful treatment and recovery. In recent years, the residual network (ResNet) has gained significant attention in the detection of breast cancer using medical images. In this paper, we propose an efficient and robust deep learning framework called FastLeakyResNet-CIR, an improved ResNet architecture, for breast cancer detection and classification. The FastLeakyResNet-CIR achieves an impressive accuracy of 98.94% when evaluated on a dataset of 7909 microscopic images of breast tumor tissue from BreakHis dataset, which outperforms the state-of-the-art methods, e.g. ResNet18, ResNet50, InceptionV3 and VGG16. The experiment results further highlight the potential of FastLeakyResNet-CIR for accurate and rapid diagnosis of breast cancer, thus facilitating effective medical treatment for patients.https://ieeexplore.ieee.org/document/10531258/Breast cancerconvolutional neural networkFastLeakyResNet-CIRmedical image classificationresidual network |
| spellingShingle | Ruiming Zeng Boan Qu Wei Liu Jianghao Li Hongshen Li Pingping Bing Shuangni Duan Lemei Zhu FastLeakyResNet-CIR: A Novel Deep Learning Framework for Breast Cancer Detection and Classification IEEE Access Breast cancer convolutional neural network FastLeakyResNet-CIR medical image classification residual network |
| title | FastLeakyResNet-CIR: A Novel Deep Learning Framework for Breast Cancer Detection and Classification |
| title_full | FastLeakyResNet-CIR: A Novel Deep Learning Framework for Breast Cancer Detection and Classification |
| title_fullStr | FastLeakyResNet-CIR: A Novel Deep Learning Framework for Breast Cancer Detection and Classification |
| title_full_unstemmed | FastLeakyResNet-CIR: A Novel Deep Learning Framework for Breast Cancer Detection and Classification |
| title_short | FastLeakyResNet-CIR: A Novel Deep Learning Framework for Breast Cancer Detection and Classification |
| title_sort | fastleakyresnet cir a novel deep learning framework for breast cancer detection and classification |
| topic | Breast cancer convolutional neural network FastLeakyResNet-CIR medical image classification residual network |
| url | https://ieeexplore.ieee.org/document/10531258/ |
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