RSDCNet: An efficient and lightweight deep learning model for benign and malignant pathology detection in breast cancer
Background Breast cancer is a leading malignant tumor among women globally, with its pathological classification into benign or malignant directly influencing treatment strategies and prognosis. Traditional diagnostic methods, reliant on manual interpretation, are not only time-intensive and subject...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2025-04-01
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| Series: | Digital Health |
| Online Access: | https://doi.org/10.1177/20552076251336286 |
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| author | Yuan Liu Haipeng Li Zhu Zhu Chen Chen Xiaojing Zhang Gongsheng Jin Hongtao Li |
| author_facet | Yuan Liu Haipeng Li Zhu Zhu Chen Chen Xiaojing Zhang Gongsheng Jin Hongtao Li |
| author_sort | Yuan Liu |
| collection | DOAJ |
| description | Background Breast cancer is a leading malignant tumor among women globally, with its pathological classification into benign or malignant directly influencing treatment strategies and prognosis. Traditional diagnostic methods, reliant on manual interpretation, are not only time-intensive and subjective but also susceptible to variability based on the pathologist's expertise and workload. Consequently, the development of an efficient, automated, and precise pathological detection method is crucial. Methods This study introduces RSDCNet, an enhanced lightweight neural network architecture designed for the automatic detection of benign and malignant breast cancer pathology. Utilizing the BreakHis dataset, which comprises 9109 microscopic images of breast tumors including various differentiation levels of benign and malignant samples, RSDCNet integrates depthwise separable convolution and SCSE modules. This integration aims to reduce model parameters while enhancing key feature extraction capabilities, thereby achieving both lightweight design and high efficiency. Results RSDCNet demonstrated superior performance across multiple evaluation metrics in the classification task. The model achieved an accuracy of 0.9903, a recall of 0.9897, an F1 score of 0.9888, and a precision of 0.9879, outperforming established deep learning models such as EfficientNet, RegNet, HRNet, and ViT. Notably, RSDCNet's parameter count stood at just 1,199,662, significantly lower than HRNet's 19,254,102 and ViT's 85,800,194, highlighting its enhanced resource efficiency. Conclusion The RSDCNet model presented in this study excels in the efficient and accurate classification of benign and malignant breast cancer pathology. Compared to traditional methods and other leading models, RSDCNet not only reduces computational resource consumption but also offers improved feature extraction and clinical interpretability. This advancement provides substantial technical support for the intelligent diagnosis of breast cancer, paving the way for more effective treatment planning and prognosis assessment. |
| format | Article |
| id | doaj-art-72c0337603ac4e88aa38486bb352cc48 |
| institution | OA Journals |
| issn | 2055-2076 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Digital Health |
| spelling | doaj-art-72c0337603ac4e88aa38486bb352cc482025-08-20T02:13:27ZengSAGE PublishingDigital Health2055-20762025-04-011110.1177/20552076251336286RSDCNet: An efficient and lightweight deep learning model for benign and malignant pathology detection in breast cancerYuan Liu0Haipeng Li1Zhu Zhu2Chen Chen3Xiaojing Zhang4Gongsheng Jin5Hongtao Li6 Department of Surgical Oncology, , Bengbu, Anhui, China Department of Mental Health, , Bengbu, Anhui, China Department of Electrocardiograph (ECG), , Bengbu, Anhui, China Department of Surgical Oncology, , Bengbu, Anhui, China Department of Surgical Oncology, , Bengbu, Anhui, China Department of Surgical Oncology, , Bengbu, Anhui, China Department of Surgical Oncology, , Bengbu, Anhui, ChinaBackground Breast cancer is a leading malignant tumor among women globally, with its pathological classification into benign or malignant directly influencing treatment strategies and prognosis. Traditional diagnostic methods, reliant on manual interpretation, are not only time-intensive and subjective but also susceptible to variability based on the pathologist's expertise and workload. Consequently, the development of an efficient, automated, and precise pathological detection method is crucial. Methods This study introduces RSDCNet, an enhanced lightweight neural network architecture designed for the automatic detection of benign and malignant breast cancer pathology. Utilizing the BreakHis dataset, which comprises 9109 microscopic images of breast tumors including various differentiation levels of benign and malignant samples, RSDCNet integrates depthwise separable convolution and SCSE modules. This integration aims to reduce model parameters while enhancing key feature extraction capabilities, thereby achieving both lightweight design and high efficiency. Results RSDCNet demonstrated superior performance across multiple evaluation metrics in the classification task. The model achieved an accuracy of 0.9903, a recall of 0.9897, an F1 score of 0.9888, and a precision of 0.9879, outperforming established deep learning models such as EfficientNet, RegNet, HRNet, and ViT. Notably, RSDCNet's parameter count stood at just 1,199,662, significantly lower than HRNet's 19,254,102 and ViT's 85,800,194, highlighting its enhanced resource efficiency. Conclusion The RSDCNet model presented in this study excels in the efficient and accurate classification of benign and malignant breast cancer pathology. Compared to traditional methods and other leading models, RSDCNet not only reduces computational resource consumption but also offers improved feature extraction and clinical interpretability. This advancement provides substantial technical support for the intelligent diagnosis of breast cancer, paving the way for more effective treatment planning and prognosis assessment.https://doi.org/10.1177/20552076251336286 |
| spellingShingle | Yuan Liu Haipeng Li Zhu Zhu Chen Chen Xiaojing Zhang Gongsheng Jin Hongtao Li RSDCNet: An efficient and lightweight deep learning model for benign and malignant pathology detection in breast cancer Digital Health |
| title | RSDCNet: An efficient and lightweight deep learning model for benign and malignant pathology detection in breast cancer |
| title_full | RSDCNet: An efficient and lightweight deep learning model for benign and malignant pathology detection in breast cancer |
| title_fullStr | RSDCNet: An efficient and lightweight deep learning model for benign and malignant pathology detection in breast cancer |
| title_full_unstemmed | RSDCNet: An efficient and lightweight deep learning model for benign and malignant pathology detection in breast cancer |
| title_short | RSDCNet: An efficient and lightweight deep learning model for benign and malignant pathology detection in breast cancer |
| title_sort | rsdcnet an efficient and lightweight deep learning model for benign and malignant pathology detection in breast cancer |
| url | https://doi.org/10.1177/20552076251336286 |
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