ItemBook Chapter

Breast Cancer Classification Using Machine Learning A Study

Title
Breast Cancer Classification Using Machine Learning A Study
Creator
Muthumari, S.; Suganthi, J.; Nagarajan, B.; Mageshwari, M.
Description
Nowadays, breast cancer is the most common disease found in women. Although many researchers and experts have aimed to discover the solution to this widespread disease, they have not determined it. In this study, the techniques that are used to find the early signs of breast cancer with the use of machine learning (ML) are discussed. ML is an emerging technology in the field of computer science and information technology, especially in disclosing medical diagnoses. ML is also used, for example, in image recognition, speech recognition, traffic prediction, virtual personal assistants, and online fraud detection. There are plenty of algorithms and techniques that are used in ML. Some of the most popular techniques are discussed in this study. 2025 selection and editorial matter, A. Malini, Surbhi Bhatia Khan, S. Kayalvizhi, and Mohammed Saraee; individual chapters, the contributors.
Source
Advances in Computational Intelligence for Health Informatics and Computer-Aided Diagnosis: Methods, Applications, and Tools;pp.193-205
Date
01-01-2025
Publisher
CRC Press
Coverage
Muthumari S., Department of Electronics and Communication Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, 603203, India; Suganthi J., Christ (Deemed to be) University, Tamil Nadu, Bengaluru, India; Nagarajan B., SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamil Nadu, 603203, India; Mageshwari M., S.M.S College of Arts and Science, Virudhunagar District, Tamil Nadu, 626123, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 978-104032652-7; 978-100348423-3;
Format
online
Language
English
Type
Book chapter

Citation

Muthumari, S.; Suganthi, J.; Nagarajan, B.; Mageshwari, M., “Breast Cancer Classification Using Machine Learning A Study,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/24342.

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