Welcome to the InfoSci Platform
Machine Learning-Aided Automatic Detection of Breast Cancer: A Survey

Machine Learning-Aided Automatic Detection of Breast Cancer: A Survey

M. Abdul Jawad, Farida Khursheed
Copyright: © 2023 |Pages: 17
ISBN13: 9781668471364|ISBN10: 1668471361|EISBN13: 9781668471371
DOI: 10.4018/978-1-6684-7136-4.ch018
Cite Chapter Cite Chapter

MLA

Jawad, M. Abdul, and Farida Khursheed. "Machine Learning-Aided Automatic Detection of Breast Cancer: A Survey." Research Anthology on Medical Informatics in Breast and Cervical Cancer, edited by Information Resources Management Association, IGI Global, 2023, pp. 330-346. https://doi.org/10.4018/978-1-6684-7136-4.ch018

APA

Jawad, M. A. & Khursheed, F. (2023). Machine Learning-Aided Automatic Detection of Breast Cancer: A Survey. In I. Management Association (Ed.), Research Anthology on Medical Informatics in Breast and Cervical Cancer (pp. 330-346). IGI Global. https://doi.org/10.4018/978-1-6684-7136-4.ch018

Chicago

Jawad, M. Abdul, and Farida Khursheed. "Machine Learning-Aided Automatic Detection of Breast Cancer: A Survey." In Research Anthology on Medical Informatics in Breast and Cervical Cancer, edited by Information Resources Management Association, 330-346. Hershey, PA: IGI Global, 2023. https://doi.org/10.4018/978-1-6684-7136-4.ch018

Export Reference

Mendeley
Favorite

Abstract

The expeditious progress of machine learning, especially the deep learning techniques, keep propelling the medical imaging community's heed in applying these techniques in improving the accuracy of cancer screening. Among various types of cancers, breast cancer is the most detrimental disease affecting women today. The prognosis of such types of disease becomes a very challenging task for radiologists due the huge number of cases together with careful and thorough examination it demands. The constraints of present CAD open up a need for new and accurate detection procedures. Deep learning approaches have gained a tremendous recognition in the areas of object detection, segmentation, image recognition, and computer vision. Precise and premature detection and classification of lesions is very critical for increasing the survival rates of patients. Recent CNN models are designed to enhance radiologists' understandings to identify even the least possible lesions at the very early stage.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.