An Ensemble Feature Subset Selection for Women Breast Cancer Classification

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Author(s): A. Kalaivani (Saveetha School of Engineering, India & Saveetha Institute of Medical and Technical Sciences, Chennai, India)
Copyright: 2021
Pages: 13
Source title: AI Innovation in Medical Imaging Diagnostics
Source Author(s)/Editor(s): Kalaivani Anbarasan (Department of Computer Science and Engineering, Saveetha School of Engineering, India & Saveetha Institute of Medical and Technical Sciences, Chennai, India)
DOI: 10.4018/978-1-7998-3092-4.ch006

Keywords: Artificial Intelligence / Computer Science & IT / Medical Information Science Reference / Medical Technologies

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Abstract

Breast cancer leads to fatal diseases both in India and America and takes the lives of thousands of women in the world every year. The patients can be easily treated if the signs and symptoms are identified at the early stages. But the symptoms identified at the final stage spreads in the human body, and most of the time, the cancer is identified at the final stage. Breast cancer detected at the early stage is treated easily rather than at the advanced stage. Computer-aided diagnosis came into existence from 2000 with high expectations to improve true positive diagnosis and reduce false positive marks. Artificial intelligence revolved in computing drives the attention of deep learning for an automated breast cancer detection and diagnosis in digital mammography. The chapter focuses on automatic feature selection algorithm for diagnosis of women breast cancer from digital mammographic images achieved through multi-layer perceptron techniques.

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An Ensemble Feature Subset Selection for Women Breast Cancer Classification

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