A comprehensive framework towards segmenting and classifying breast cancer patients using deep learning and Aquila optimizer

Abstract

Breast cancer is among the major frequent types of cancer worldwide, causing a significant death rate every year. It is the second most prevalent malignancy in Egypt. With the increasing number of new cases, it is vital to diagnose breast cancer in its early phases to avoid serious complications and deaths. Therefore, routine screening is important. With the current evolution of deep learning, medical imaging became one of the interesting fields. The purpose of the current work is to suggest a hybrid framework for both the classification and segmentation of breast scans. The framework consists of two phases, namely the classification phase and the segmentation phase. In the classification phase, five different CNN architectures via transfer learning, namely MobileNet, MobileNetV2, NasNetMobile, VGG16, and VGG19, are applied. Aquila optimizer is used for the calculation of the optimal hyperparameters of the different TL architectures. Four different datasets representing four different modalities (i.e., MRI, Mammographic, Ultrasound images, and Histopathology slides) are used for training purposes. The framework can perform both binary- and multi-class classification. In the segmentation phase, five different structures, namely U-Net, Swin U-Net, Attention U-Net, U-Net++, and V-Net, are applied to identify the region of interest in the ultrasound breast images. The reported results prove the efficiency of the suggested framework against current state-of-the-art studies.