ADBNet: An Attention-Guided Deep Broad Convolutional Neural Network for the Classification of Breast Cancer Histopathology Images

Abstract

Breast cancer is one of the leading causes of death among women. Timely diagnosis improves patient survival rates. However, classifying breast cancer histopathological slides using deep learning faces challenges. These challenges include limited datasets, multiple magnification factors, interference from irrelevant features, complex variations between classes, and issues with model interpretability. To address these challenges, we introduce a new attention-guided deep broad convolutional neural network (ADBNet). The ADBNet has a modified convolutional block attention module that focuses on selective features while suppressing irrelevant ones. It also has a deep broad block that enhances the network’s resilience to various magnification factors. Additionally, we employ a generative adversarial network combined with diffusion to expand the dataset with expertly validated images. This enriches the dataset for classifying similar cancer subtypes. The ADBNet achieves remarkable image level recognition accuracy: 99.33% at 40x magnification, 99.52% at 100x, 99.13% at 200x, and 99.06% at 400x magnification. It also attains high patient level recognition accuracy: 99.05% at 40x, 99.15% at 100x, 99.03% at 200x, and 98.60% at 400x magnification. Impressively, for magnification independent classification, our approach achieves 99.36% image level and 99.28% patient level recognition accuracy. We evaluate the proposed model on publicly available datasets, including breast histopathology images, LC25000, and Cifar-10. The results surpass the performance of existing methods. This reinforces the efficacy and potential of the ADBNet for classifying breast cancer histopathological images.

Keywords

• Deep learning
• histopathological slides
• convolutional neural network
• convolutional block attention module
• deep broad block
• magnification factor