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 language | English |
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Title of host publication | 2024 Global Conference on Wireless and Optical Technologies (GCWOT) |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 1-7 |
Number of pages | 7 |
Publication status | Published - 25 Sept 2024 |