Early breast cancer diagnosis using cogent activation function-based deep learning implementation on screened mammograms

Abstract

Breast cancer is detected in one out of eight females worldwide. Principally biomedical image processing techniques work with images captured by a microscope and then analyzed with the help of different algorithms and methods. Instead of microscopic image diagnosis, machine learning algorithms are now incorporated to detect and diagnose therapeutic imagery. Computer-aided mechanisms are used for better efficiency and reliability compared with manual pathological detection systems. Machine learning algorithms detect tumors by extracting features through a convolutional neural network (CNN) and then classifying them using a fully connected network. As Machine learning does not require prior expertise, it is profoundly used in biomedical imaging. This article has customized a convolutional neural network by mathematical modeling of a proposed activation function. We have obtained an appreciable prediction accuracy of up to 99%, along with a precision of 0.97. © 2022 Wiley Periodicals LLC.