Transfer learning-assisted multi-resolution breast cancer histopathological images classification

Breast cancer is one of the leading death cause among women nowadays. Several methods have been proposed for the detection of breast cancer. Various machine learning-based automatic diagnosis systems have been developed known as Computer Aided Diagnostics (CAD) systems. Initial CAD systems were based on machine learning algorithms however due to the automatic feature extraction ability of convolutional neural networks (CNN)-based deep learning models are widely adopted. Deep learning is widely used in various fields. Healthcare is one of the essential field that deep learning has transformed. Another common issue faced by the patients is difference of opinion among different pathologists and medical practitioners. Such human errors often lead to misleading or delayed judgment, which may be fatal to human life. To improve decision consistency, efficiency, and error reduction, researchers in the field of healthcare are using deep learning-based approaches and achieved state of art results. In this study, a deep learning (DL) and transfer learning-based approach is proposed to classify histopathological images for breast cancer diagnosis. In this study, we have adopted patch selection approach to classify breast histopathological images on small number of training images using transfer learning without losing the performance. Initially, patches are extracted from Whole Slide Images and fed into the CNN for features extraction. Based on these features, the discriminative patches are selected and then fed to Efficient-Net architecture pre-trained on ImageNet dataset. Features extracted from Efficient-Net architecture are also used to train a SVM classifier. The proposed model outperforms the baseline methods in terms of multiple performance measures.

Automated summary

Breast cancer is a leading cause of death among women, and machine learning and deep learning techniques are widely used in its diagnosis, improving decision consistency and reducing errors in the healthcare field.