Deep Stacked Sparse Autoencoders – A Breast Cancer Classifier

Muhammad Asif Munir; Muhammad Aqeel Aslam; Muhammad Shafique; Rauf Ahmed; Zafar Mehmood

doi:10.22581/muet1982.2201.05

Muhammad Asif Munir Department of Electrical Engineering, Swedish College of Engineering and Technology, Rahim Yar Khan, Pakistan.
Muhammad Aqeel Aslam Department of Electrical Engineering, Swedish College of Engineering and Technology, Rahim Yar Khan, Pakistan. Department of Instrument Science and Engineering, School of Electronic Information and Electrical Engineering, Engineering Research Center for Intelligent Diagnosis and Treatment Instrument, Shanghai Jiao Tong University, Shanghai, China.
Muhammad Shafique Department of Electrical Engineering, Swedish College of Engineering and Technology, Rahim Yar Khan, Pakistan.
Rauf Ahmed 1 Department of Electrical Engineering, Swedish College of Engineering and Technology, Rahim Yar Khan, Pakistan.
Zafar Mehmood Department of Electrical Engineering, Swedish College of Engineering and Technology, Rahim Yar Khan, Pakistan.

DOI: https://doi.org/10.22581/muet1982.2201.05

Abstract

Breast cancer is among one of the non-communicable diseases that is the major cause of women's mortalities around the globe. Early diagnosis of breast cancer has significant death reduction effects. This chronic disease requires careful and lengthy prognostic procedures before reaching a rational decision about optimum clinical treatments. During the last decade, in Computer-Aided Diagnostic (CAD) systems, machine learning and deep learning-based approaches are being implemented to provide solutions with the least error probabilities in breast cancer screening practices. These methods are determined for optimal and acceptable results with little human intervention. In this article, Deep Stacked Sparse Autoencoders for breast cancer diagnostic and classification are proposed. Anticipated algorithms and methods are evaluated and tested using the platform of MATLAB R2017b on Breast Cancer Wisconsin (Diagnostic) Data Set (WDBC) and achieved results surpass all the CAD techniques and methods in terms of classification accuracy and efficiency.