Original scientific paper

https://doi.org/10.20532/cit.2024.1005848

SSResNeXt: A Novel Deep Learning Architecture for Multi-class Breast Cancer Pathological Image Classification

Bo Xu ; School of Information Science, Guangdong University of Finance and Economics, China
Faming Li ; School of Information Science, Guangdong University of Finance and Economics, China
Ying Wu ; The First Affiliated Hospital of Jinan University, Jinan University, China

Abstract

Multi-class classification of breast cancer pathological images remains challenging due to complex image features and limited datasets. This study proposes SSResNeXt, a novel deep learning architecture incorporating a new Small-SE-ResNeXt Block with asymmetric convolutions and channel attention mechanisms. Evaluated on the BreaKHis dataset, SSResNeXt achieves state-of-the-art performance with accuracies of 95.2%, 94.0%, 92.7%, and 93.5% for 40X, 100X, 200X, and 400X magnification scales, respectively. Comparative experiments demonstrate SSResNeXt's superior performance over existing models, including ResNet and Swin Transformer variants. The proposed architecture offers improved feature extraction capabilities for breast cancer pathological images without significantly increasing model complexity, providing a promising tool for computer-aided diagnosis systems.

Keywords

deep learning; multi-classification; breast cancer; pathological images

Hrčak ID:

321567

URI

https://hrcak.srce.hr/321567

Publication date:

30.9.2024.

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