Hybrid Convolution-Transformer models for breast cancer classification using histopathological images

Breast cancer threatens the public health as it is among the leading causes of women death due to unawareness and diagnosis at the late stages. The detection of this cancer in its early stage is decisive to decrease mortality rates . Deep learning techniques are effective in analysis of medical images and achieve high performance in detecting the abnormal features, and classify them. Therefore, these methods are becoming increasingly popular in breast cancer diagnosis. Convolutional Neural Networks (CNNs) are commonly used for medical image analysis, but Vision transformers (ViTs ) are becoming more popular due to their excellent performance. However, ViTs still fall behind state-of-the-art convolutional networks. To overcome these limitations, many researchers have proposed a new approach that combines the advantages of CNNs and Transformers. This new approach overcomes the limitations of each by extracting low-level features, strengthening locality, and establishing long-range dependencies. In this study, the Hybrid Conv-Transformer approach was used to extract features from the BreakHis dataset of histopathological images. Coatnet and ConvMixer models were then used to classify the images into two binary classification based on both magnification-dependent and magnification-independent categories. The findings indicated that the suggested models exceeded prior models and recent deep learning techniques on the BreakHis dataset.