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RSDCNet: An efficient and lightweight deep learning model for benign and malignant pathology detection in breast
Yuan Liu1, Haipeng Li2, Zhu Zhu3
1Department of Surgical Oncology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui, China.
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.
