ISSN: 2572-4118

Breast Cancer: Current Research
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  • Case Report   
  • Breast Can Curr Res 2023, Vol 8(4): 4
  • DOI: 10.4172/2572-4118.1000200

A comparison of Traditional Machine Learning with Early Diagnosis of Breast Cancer

Gonzales Martinez*
Department of Oncology and Breast surgery Sciences, Netherlands
*Corresponding Author : Gonzales Martinez, Department of Oncology and Breast surgery Sciences, Netherlands, Email: gonzales.martinez@rug.nl

Received Date: Aug 01, 2023 / Published Date: Aug 28, 2023

Abstract

Breast cancer, a prevalent global health issue, demands timely diagnosis for effective treatment. This article delves into the realm of early breast cancer detection, comparing traditional diagnostic methods with the innovative application of machine learning (ML) techniques. While traditional methods such as mammography and histopathological analysis have been instrumental, ML’s potential to enhance accuracy and efficiency in early diagnosis is gaining prominence. This article evaluates the juxtaposition of these methodologies, highlighting ML’s contributions in image analysis, risk assessment, pathology analysis, data fusion, and pattern recognition. By examining the strengths, challenges, and potential synergies between traditional and ML approaches, this article underscores the evolving landscape of breast cancer diagnosis.

Citation: Martinez G (2023) A comparison of Traditional Machine Learning withEarly Diagnosis of Breast Cancer. Breast Can Curr Res 8: 200. Doi: 10.4172/2572-4118.1000200

Copyright: © 2023 Martinez G. This is an open-access article distributed underthe terms of the Creative Commons Attribution License, which permits unrestricteduse, distribution, and reproduction in any medium, provided the original author andsource are credited.

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