Unlocking the Potential Thorough Analysis of Machine Learning for Breast Cancer Diagnosis

Author:

Bhaskar P.,Syed Tahaseen,Varsha Daka Hima,Kumar Theegala Nikhil,Tulluri Manikanta

Abstract

To forecast breast cancer with the goal of giving a thorough rundown of current developments in the area. Given that breast cancer is among the world's leading causes of mortality for women; improving patient outcomes requires early detection. This study looks into the ability to predict outcomes using a variety of machine learning (ML) models, including random forests, logistic regression, support vector machines, decision trees, k- nearest neighbours, and deep learning neural networks, in predicting the incidence of breast cancer from patient data, including genetic markers, imaging results, and demographics. Aims to provide a comprehensive analysis of presetn advancements, obstacles, and prospects in the field of CNN-based techniques for breast cancer identification. The review begins by outlining the urgent need for reliable and accurate diagnostic methods for breast cancer, highlighting the critical role that early identification plays in enhancing patient outcomes. Which delves into the intricate architecture of CNNs, revealing its unique applicability to mammography image analysis as well as their innate advantages in image classification tasks. Important topics of discussion include the various CNN architectures used for two- and three- dimensional (2D) imaging methods used in breast cancer diagnosis.

Publisher

International Journal of Innovative Science and Research Technology

Reference20 articles.

1. C. Dubey, N. Shukla, D. Kumar, A. K. Singh and V. K. Dwivedi, "Breast Cancer Modeling and Prediction Combining Machine Learning and Artificial Neural Network Approaches," 2022 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), Greater Noida, India, 2022, pp. 119-124, doi: 10.1109/ICCCIS56430.2022.10037709.

2. A. Rovshenov and S. Peker, "Performance Comparison of Different Machine Learning Techniques for Early Prediction of Breast Cancer using Wisconsin Breast Cancer Dataset," 2022 3rd International Informatics and Software Engineering Conference (IISEC), Ankara, Turkey, 2022, pp. 1-6, doi: 10.1109/IISEC56263.2022.9998248.

3. A. Rovshenov and S. Peker, "Performance Comparison of Different Machine Learning Techniques for Early Prediction of Breast Cancer using Wisconsin Breast Cancer Dataset," 2022 3rd International Informatics and Software Engineering Conference (IISEC), Ankara, Turkey, 2022, pp. 1-6, doi: 10.1109/IISEC56263.2022.9998248.

4. M. Gupta and B. Gupta, "A Comparative Study of Breast Cancer Diagnosis Using Supervised Machine Learning Techniques," 2018 Second International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, 2018, pp. 997-1002, doi: 10.1109/ICCMC.2018.8487537.

5. E. A. Bayrak, P. Kırcı and T. Ensari, "Comparison of Machine Learning Methods for Breast Cancer Diagnosis," 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT), Istanbul, Turkey, 2019, pp. 1-3, doi: 10.1109/EBBT.2019.8741990.

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Bhramari Pranayama and Thoracic Mobility Exercises for Moderate Chronic Obstructive Pulmonary Disease (COPD): A Case Study;International Journal of Innovative Science and Research Technology (IJISRT);2024-04-29

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3