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All Journal International Journal of Electrical and Computer Engineering
Kim Gaik Tay
University Tun Hussein Onn Malaysia
Computer Science & IT Electrical & Electronics Engineering

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Image processing and machine learning techniques used in computer-aided detection system for mammogram screening - a review Susama Bagchi; Kim Gaik Tay; Audrey Huong; Sanjoy Kumar Debnath
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (574.663 KB) | DOI: 10.11591/ijece.v10i3.pp2336-2348

Abstract

This paper aims to review the previously developed Computer-aided detection (CAD) systems for mammogram screening because increasing death rate in women due to breast cancer is a global medical issue and it can be controlled only by early detection with regular screening. Till now mammography is the widely used breast imaging modality. CAD systems have been adopted by the radiologists to increase the accuracy of the breast cancer diagnosis by avoiding human errors and experience related issues. This study reveals that in spite of the higher accuracy obtained by the earlier proposed CAD systems for breast cancer diagnosis, they are not fully automated. Moreover, the false-positive mammogram screening cases are high in number and over-diagnosis of breast cancer exposes a patient towards harmful overtreatment for which a huge amount of money is being wasted. In addition, it is also reported that the mammogram screening result with and without CAD systems does not have noticeable difference, whereas the undetected cancer cases by CAD system are increasing. Thus, future research is required to improve the performance of CAD system for mammogram screening and make it completely automated.
Co-Authors Audrey Huong Sanjoy Kumar Debnath Susama Bagchi