Breast Cancer Histopathology Classification using CBAM-EfficientNetV2 with Transfer Learning
This work addresses the problem of improving diagnostic precision for breast cancer detection, which is critical for early detection and patient outcomes, but it is incremental as it builds on existing EfficientNetV2 and CBAM architectures.
The study tackled breast cancer histopathology image classification by introducing a novel approach using EfficientNetV2 models with CBAM, achieving a peak accuracy of 99.01% and an F1-score of 98.31% at 400X magnification on the BreakHis dataset, outperforming state-of-the-art methods.
Breast cancer histopathology image classification is critical for early detection and improved patient outcomes. 1 This study introduces a novel approach leveraging EfficientNetV2 models, to improve feature extraction and focus on relevant tissue regions. The proposed models were evaluated on the BreakHis dataset across multiple magnification scales (40X, 100X, 200X, and 400X). 2 Among them, the EfficientNetV2-XL with CBAM achieved outstanding performance, reaching a peak accuracy of 99.01 percent and an F1-score of 98.31 percent at 400X magnification, outperforming state-of-the-art methods. 3 By integrating Contrast Limited Adaptive Histogram Equalization (CLAHE) for preprocessing and optimizing computational efficiency, this method demonstrates its suitability for real-time clinical deployment. 3 The results underscore the potential of attention-enhanced scalable architectures in advancing diagnostic precision for breast cancer detection.