Deep Learning Approach for Breast Cancer Detection and Classification using Histopathological Images

Abstract

Accurate detection of breast cancer (BC) is one of the effective way newlineto reduce the mortality rate of woman. Histopathology image is an newlineimportant image modality for detection of breast cancer. The aim of newlinepresent work is to development of automatic and accurate system for newlinedetection and classification of BC. newlineFirst proposed system consists of image enhancement, localization newlineand segmentation of suspicious cell tissues, and classification steps. newlineDeer Canid optimization-based deep Convolutional Neural Network newline(DeCdO-based deep CNN) architecture is employed for classification newlineof breast cancer into normal, benign, and malignant categories. newlineHighest 93.06% accuracy, 94.34% precision, 93.54% recall, and newline92.98% f1 measure is obtained while using BreHistoID dataset. newlineSecond proposed system consists of image preprocessing, newlinesegmentation of suspicious cell tissues, Feature extraction and newlineclassification steps. The Shuffled Shepherd Deer Hunting newlineOptimization-based Deep Neural Network (SSDHO-based DNN) is newlineutilized to classify breast cancer, enabling more comprehensive and newlineaccurate categorization. It classified Breast Cancer (BC) images into newlinesix various classes, like tubule, non-tubule, mitosis, apoptosis, tumor newlinenuclei, and non-tumor nuclei. Classification method is demonstrated newlinethrough rigorous evaluation, where accuracy, precision, sensitivity, newlineand specificity, attained values of 95.61%, 82.32%, 79.03%, and newline94.26%, respectively using BreHistID dataset. newlineThird proposed system consists of image enhancement, localization newlineand segmentation of blood cell, and classification steps. Pre-trianed newlinedeep CNN is used to automatically categorise breast cancer cancers newlineinto benign, malignant, and normal tumors. Four pre-trained namely, newlineVggNet-19, AlexNet, ResNet-18, and Inception-V3 models are used newlinefor classification. The classifier performance is measured using newlinemetrics value of 97.50% accuracy, 83.54% precision, 85.73% newlinesensitivity, and 92.87% specificity using BreKHis dataset which is newlinereliable compared to some state of the art method. newline