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.
Highlights
Health concerns and risks are at ramps with the fast-growing population around the globe
In [1],World Health Organization (WHO) had reported that active and healthy lifestyle may contribute to reduction and prevention of Non-Communicable Diseases (NCD) and it was observed that 3 in 4 adolescents and 1 in 4 adults didn’t meet the physical activity standard set by WHO and its affects are more prevalent in developed countries
In [20], two machine learning techniques, namely Naive Bayes and the K-Nearest Neighbor (KNN) were evaluated on the Wisconsin breast cancer dataset for the tumor classification purpose and compared their performance as KNN achieving 97.51% with the least error rate while NB classifier having 96.19 % accuracy
Summary
Health concerns and risks are at ramps with the fast-growing population around the globe. Death tolls in women are at peaks with breast cancer and about 11.6% of total cancer deaths are contributed by women breast cancers These avoidable life risks require early diagnostic procedures and feasible treatments. According to [6], in 44% of World Health Organization associated countries, the availability of medical doctors per 1000 people is less than 1. These statistics are becoming more alarming regarding pathologists, even in the United States. In the artificial intelligence domain, machine learning and deep learning-based algorithms are playing a vital role to expedite the diagnostic process with the help of data scientists having the least medical field knowledge.
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