Advancing Breast Cancer Detection: SE-Conformer Framework for Malignancy Detection in Histopathology Images
Lekha S. Nair, Kurmapu Amarnath, Jyothisha J. Nair · IEEE Access · 2025
Globally, breast cancer is the second most lethal form of cancer among women, and has high rates of incidence and mortality. Early detection is crucial in improving survival rates. As deep learning technologies continue to advance, they are increasingly being leveraged to automate pathological image classification, primarily through conventional models. Traditional models excel at processing images in which objects are clearly defined against their backgrounds, but they struggle with pathological images because the boundaries between tumor lesions and surrounding tissues are often indistinct. Hence, models that can learn the minute details in these kinds of images are very essential.We propose a hybrid approach that integrates convolutional networks with attention mechanisms to achieve high classification performance while reducing computational complexity. To enhance feature representation in convolutional neural networks, we introduced an improved convolutional block, termed the SE-Res-Conv Block, which incorporates Squeeze-and-Excitation (SE) attention mechanisms within a residual convolutional framework. The extracted features are then processed by a Conformer block, which further refines them by emphasizing the most relevant regions in the input feature map. Finally, a dense output layer is used for classification.The proposed model is evaluated on both the BACH dataset and all four magnification levels of the BreakHis dataset. Experimental results, obtained through K-fold cross-validation, indicate that the model performs optimally on 200× magnification images, achieving an average accuracy, precision, and recall of 0.97. These results underscore the model’s effectiveness and its potential applicability in medical image classification tasks.