Detection and Classification of Histopathological Breast Images Using a Fusion of CNN Frameworks

Author:

Rafiq Ahsan1ORCID,Chursin Alexander2,Awad Alrefaei Wejdan3,Rashed Alsenani Tahani4,Aldehim Ghadah5,Abdel Samee Nagwan6ORCID,Menzli Leila Jamel5ORCID

Affiliation:

1. School of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

2. Higher School of Industrial Policy and Entrepreneurship, RUDN University, 6 Miklukho-Maklaya St, Moscow 117198, Russia

3. Department of Programming and Computer Sciences, Applied College in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj 16245, Saudi Arabia

4. Department of Biology, College of Sciences in Yanbu, Taibah University, Yanbu 46522, Saudi Arabia

5. Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia

6. Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia

Abstract

Breast cancer is responsible for the deaths of thousands of women each year. The diagnosis of breast cancer (BC) frequently makes the use of several imaging techniques. On the other hand, incorrect identification might occasionally result in unnecessary therapy and diagnosis. Therefore, the accurate identification of breast cancer can save a significant number of patients from undergoing unnecessary surgery and biopsy procedures. As a result of recent developments in the field, the performance of deep learning systems used for medical image processing has showed significant benefits. Deep learning (DL) models have found widespread use for the aim of extracting important features from histopathologic BC images. This has helped to improve the classification performance and has assisted in the automation of the process. In recent times, both convolutional neural networks (CNNs) and hybrid models of deep learning-based approaches have demonstrated impressive performance. In this research, three different types of CNN models are proposed: a straightforward CNN model (1-CNN), a fusion CNN model (2-CNN), and a three CNN model (3-CNN). The findings of the experiment demonstrate that the techniques based on the 3-CNN algorithm performed the best in terms of accuracy (90.10%), recall (89.90%), precision (89.80%), and f1-Score (89.90%). In conclusion, the CNN-based approaches that have been developed are contrasted with more modern machine learning and deep learning models. The application of CNN-based methods has resulted in a significant increase in the accuracy of the BC classification.

Publisher

MDPI AG

Subject

Clinical Biochemistry

Cited by 6 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. Machine Learning for Early Breast Cancer Detection;Journal of Engineering and Science in Medical Diagnostics and Therapy;2024-07-26

2. Explainable Soft Attentive EfficientNet for breast cancer classification in histopathological images;Biomedical Signal Processing and Control;2024-04

3. Revolutionizing Breast Cancer Diagnosis: A Concatenated Precision through Transfer Learning in Histopathological Data Analysis;Diagnostics;2024-02-14

4. A self-learning deep neural network for classification of breast histopathological images;Biomedical Signal Processing and Control;2024-01

5. Histopathology Image-Based Cancer Classification Utilizing Transfer Learning Approach;2023 13th International Conference on Computer and Knowledge Engineering (ICCKE);2023-11-01

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