HYBRID OPTIMIZATION ENABLED SEGMENTATION AND DEEP LEARNING FOR BREAST CANCER DETECTION AND CLASSIFICATION USING HISTOPATHOLOGICAL IMAGES

Abstract

Breast cancer detection is a highly fatal disease and is normally detected considering histopathological images (HPIs). However, the complexity of the HPIs makes it challenging to detect breast cancer accurately. Further, manual detection is highly time-consuming and subjective and depends on the experience of the medical professionals. To overcome these issues, an effective deep learning (DL) method for detecting breast cancer from HPIs is proposed. Here, the proposed approach is realized using various processes, such as pre-processing, blood cell segmentation, feature extraction, and classification. Segmentation is accomplished using the SegAN, and classification is performed using the deep convolutional neural network (DCNN). Both networks are trained using the proposed invasive water Ebola optimization (IWEO) algorithm. The efficiency of breast cancer detection is improved by using various features, such as shape features, histogram of gradients (HOG) and local gradient patterns (LGP). Further, the IWEO-DCNN is inspected for its dominance by considering measures, such as accuracy, test negative rate (TNR) and test positive rate (TPR), and the experimental results show that the IWEO-DCNN attained a maximal accuracy of 0.963, TNR of 0.963 and TPR of 0.950.