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

• Published in: International journal of imaging systems and technology 2022-07, Vol.32 (4), p.1101-1118
• Main Authors: Rajput, Gunjan, Agrawal, Shashank, Biyani, Kunika, Vishvakarma, Santosh Kumar
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial neural networks; breast cancer classification; convolutional neural network; Deep learning; detection; Diagnosis; Feature extraction; Image processing; Machine learning; Medical imaging; Mias dataset
• ISSN: 0899-9457; 1098-1098
• DOI: 10.1002/ima.22701

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.