A Deep Learning Based Methodological Analysis for Breast Cancer Classification

International Journal of Electronics and Communication Engineering

© 2023 by SSRG - IJECE Journal

Volume 10 Issue 6

Year of Publication : 2023

Authors : T. Rajendran, G. Divya, S. Sridhar, T. Anitha

10.14445/23488549/IJECE-V10I6P106

How to Cite?

T. Rajendran, G. Divya, S. Sridhar, T. Anitha, "A Deep Learning Based Methodological Analysis for Breast Cancer Classification," SSRG International Journal of Electronics and Communication Engineering, vol. 10,  no. 6, pp. 52-68, 2023. Crossref, https://doi.org/10.14445/23488549/IJECE-V10I6P106

Abstract:

Breast cancer (BC) is the world’s most common and rapidly spreading disease. BC can be controlled, reducing the death rate if detected early. As a result, some researchers have recently developed deep-learning-based efficient algorithms for predicting cancer cell growth using different modalities of medical imaging, for example, mammograms, tomosynthesis, MRI ultrasound, etc., for their efficiency and accuracy. Only a few review articles on BC diagnosis synthesize some existing studies. These investigations, unfortunately, are unable to identify new structures and modalities in the diagnosis of BC. The changing frameworks of DL for BC detection are the subject of this review. This assessment explores the strengths and limitations of earlier deep-learning (DL) based methods, investigates the datasets employed, and examines image preprocessing approaches based on different medical imaging modalities. The performance results with evaluation, obstacles, and further enhancement are also provided.

Keywords:

Breast cancer, Dataset, Deep learning, Image classification, Medical imaging modalities.

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