Comprehensive Study for Breast Cancer Using Deep Learning and Traditional Machine Learning

Abstract

Breast cancer is one of the most dangerous diseases and the second largest cause of women cancer death. Techniques and methods have been adopted for early indications of the disease signs as it’s the only effective way of managing breast cancer in women. This review explores the techniques used for breast cancer in Computer-Aided Diagnosis (CAD) using image analysis, deep learning and traditional machine learning. It primarily gives an introduction to the various strategies of machine learning, followed by an explanation of the various deep learning techniques and particular architectures for breast cancer detection and their classification. After the review, the researcher recommended the need for the inclusion of deep learning in machine learning because it performs multi-functions in enabling medical diagnosis. Also, it is important to involve the integration of more than learning methods in medical learning to improve the process of medical diagnostic imaging and their benefits and limitations, recent advancements and development are discussed by reviewing the existing secondary sources. This study reviews papers published from 2015 (early publications on breast cancer) to 2021. This paper is a review of the latest works and techniques have done in the field with the future trends and problems in breast cancer categorization and diagnosis. REFERENCE: AHMED, L., IQBAL, M. M., ALDABBAS, H., KHALID, S., SALEEM, Y. & SAEED, S. 2020. Images data practices for semantic segmentation of breast cancer using deep neural network. Journal of Ambient Intelligence and Humanized Computing, 1-17. AKBAR, S., AKRAM, M. U., SHARIF, M., TARIQ, A. & KHAN, S. A. 2018. Decision support system for detection of hypertensive retinopathy using arteriovenous ratio. Artificial intelligence in medicine, 90, 15-24. 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