Machine Learning Methods for Early Detection of Breast Cancer: A Short Review

Mahaveer Rathi, Toufique Ahmed Soomro, Enrique Nava Baro, Bhawani Shankar Chowdhry

Research output: Book chapter/Published conference paperConference paperpeer-review

Abstract

Cancer, characterized by uncontrolled cell proliferation, is a significant global health problem. Breast cancer, the most common cancer among women, can lead to reduced mortality rates through early detection. Medical imaging plays a crucial role in the identification and diagnosis of breast cancer by providing vital diagnostic information. This article provides an overview of recent advances in the field, focusing on using machine learning (ML) and deep learning (DL) techniques for breast cancer detection. It reviews breast cancer classification using various ML and DL methods in different imaging modalities, analyzing the differences between these modalities using datasets from multiple studies. Finally, the article discusses the challenges related to the classification and detection of breast cancer, highlighting the effectiveness of various approaches in this area.
Original languageEnglish
Title of host publication2024 Global Conference on Wireless and Optical Technologies (GCWOT)
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1-7
Number of pages7
Publication statusPublished - 25 Sept 2024

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