Breast Cancer Diagnosis in Digital Mammography Images Using Automatic Detection for the Region of Interest

Author:

Ramadan Saleem Z.1ORCID,El-Banna Mahmoud1ORCID

Affiliation:

1. Department of Industrial Engineering, German Jordanian University, Mushaqar, Amman 11180, Jordan

Abstract

Background: One of the early screening methods of breast cancer that is still used today is mammogram due to its low cost. Unfortunately, this low cost accompanied with low performance rate also. Methods: The low performance rate in mammograms is associated with low capability in determining the best region from which the features are extracted. Therefore, we offer an automatic method to detect the Region of Interest in the mammograms based on maximizing the area under receiver operating characteristic curve utilizing Genetic Algorithms. : The proposed method had been applied to the MIAS mammographic database, which is widely used in literature. Its performance had been evaluated using four different classifiers; Support Vector Machine, Naïve Bayes, K-Nearest Neighbor and Logistic Regression classifiers. Results & Conclusion: The results showed good classification performances for all the classifiers used due to the rich information contained in the features extracted from the automatically selected Region of Interest.

Publisher

Bentham Science Publishers Ltd.

Subject

Radiology, Nuclear Medicine and imaging

Cited by 4 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. Efficient breast cancer detection via cascade deep learning network;International Journal of Intelligent Networks;2023

2. Layered Deep learning for Improved Breast Cancer Detection;2022 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS);2022-11-22

3. Using Convolutional Neural Network with Cheat Sheet and Data Augmentation to Detect Breast Cancer in Mammograms;Computational and Mathematical Methods in Medicine;2020-10-28

4. Methods Used in Computer-Aided Diagnosis for Breast Cancer Detection Using Mammograms: A Review;Journal of Healthcare Engineering;2020-03-12

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