Deep Stacked Sparse Autoencoders – A Breast Cancer Classifier
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 ration...
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| Vydané v: | Mehran University research journal of engineering and technology Ročník 41; číslo 1; s. 41 - 52 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Mehran University of Engineering and Technology
01.01.2022
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| ISSN: | 0254-7821, 2413-7219 |
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| 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. |
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| AbstractList | 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. Keywords: Breast Cancer, Deep Stacked Autocoders, Diagnostic Systems, 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. |
| Audience | Academic |
| Author | Munir, Muhammad Asif Shafique, Muhammad Aslam, Muhammad Aqeel Ahmed, Rauf Mehmood, Zafar |
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| SubjectTerms | Algorithms Breast cancer Cable television broadcasting industry Cancer Chronic diseases Communicable diseases Diagnosis Machine learning Mortality United States Wisconsin |
| Title | Deep Stacked Sparse Autoencoders – A Breast Cancer Classifier |
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