Deep Stacked Sparse Autoencoders – A Breast Cancer Classifier

• Published in: Mehran University research journal of engineering and technology Vol. 41; no. 1; pp. 41 - 52
• Main Authors: Munir, Muhammad Asif, Aslam, Muhammad Aqeel, Shafique, Muhammad, Ahmed, Rauf, Mehmood, Zafar
• Format: Journal Article
• Language: English
• Published: Mehran University of Engineering and Technology 01.01.2022
• Subjects: Algorithms; Breast cancer; Cable television broadcasting industry; Cancer; Chronic diseases; Communicable diseases; Diagnosis; Machine learning; Mortality; United States; Wisconsin; United States; Wisconsin
• ISSN: 0254-7821; 2413-7219; 2413-7219
• DOI: 10.22581/muet1982.2201.05

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.