Enhancing the accuracy of breast cancer detection and determination of risk factor by using the backpropagation network theory and SVM: Machine learning
*N. Bindu MadhaviCorresponding authordr.bindumadhavi@kluniversity.inKL Business SchoolKoneru Lakshmaiah Education Foundation (Deemed to be University)Vaddeswaram, Andhra Pradesh, IndiaView full profile → , Sushil Doharesdohare@jazanu.edu.saDepartment of EpidemiologyCollege of Public Health and Tropical MedicineJazan UniversitySaudi ArabiaView full profile → , G. N. R. Prasadgnrp@cbit.ac.inDepartment of MCAChaitanya Bharathi Institute of Technology (A)Gandipet, Hyderabad, 500075, IndiaView full profile → , D. Vijendra Babudrdvijendrababu@gmail.comSchool of Electronics EngineeringVellore Institute of TechnologyVellore, Tamil Nadu, 632014, IndiaView full profile → , Abdul Rahman Mohammed Al-AnsariHaman.alansari@gmail.comDepartment of SurgerySalmanyia HospitalBahrianView full profile →
* Corresponding author · click or hover a name for details
- Published Online:
- 11 Aug 2023
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-1365
- Pages:
- 455–465
Abstract
Keywords
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References
[1] Lin, R.-H.; Kujabi, B.K.; Chuang, C.-L.; Lin, C.-S.; Chiu, C.-J. Application of Deep Learning to Construct Breast Cancer Diagnosis Model. Appl. Sci. 2022, 12, 1957. https://doi.org/10.3390/app12041957.
[2] Luuk Balkenende, Jonas Teuwen, Ritse M. Mann, Application of Deep Learning in Breast Cancer Imaging, Seminars in Nuclear Medicine, Volume 52, Issue 5, Pages 584-596 (2022), ISSN 0001-2998, https://doi.org/10.1053/j.semnuclmed.2022.02.003.
[3] Shen, L., Margolies, L.R., Rothstein, J.H. et. al. Deep Learning to Improve Breast Cancer Detection on Screening Mammography. Sci Rep 9, 12495 (2019). https://doi.org/10.1038/s41598-019-48995-4.
[4] Mridha MF, Hamid MA, Monowar MM, Keya AJ, Ohi AQ, Islam MR, Kim JM. A Comprehensive Survey on Deep-Learning-Based Breast Cancer Diagnosis. Cancers (Basel). 13(23):6116 (2021 Dec 4). doi: 10.3390/cancers13236116. PMID: 34885225; PMCID: PMC8656730.
[5] Arooj Sahar, Atta-ur-Rahman , Zubair Muhammad, Khan Muhammad Farhan, Alissa Khalid, Khan Muhammad Adnan, Mosavi Amir, Breast Cancer Detection and Classification Empowered With Transfer Learning, Frontiers in Public Health, Volume 10, (2022) ISSN- 2296-2565, 10.3389/fpubh.2022.924432, URL=https://www.frontiersin.org/articles/10.3389/fpubh.2022.924432.
[6] BramahHazela, J. Hymavathi, T. Rajasanthosh Kumar, S. Kavitha, D. Deepa, Sachin Lalar, Prabakaran Karunakaran, “Machine Learning: Supervised Algorithms to Determine the Defect in High-Precision Foundry Operation”, Journal of Nanomaterials, vol. 2022, Article ID 1732441, 9 pages (2022). https://doi.org/10.1155/2022/1732441.
[7] Dewangan, K.K., Dewangan, D.K., Sahu, S.P. et. al. Breast cancer diagnosis in an early stage using novel deep learning with hybrid optimization technique. Multimed Tools Appl 81, 13935–13960 (2022). https://doi.org/10.1007/s11042-022-12385-2.
[8] Saad Awadh Alanazi, M. M. Kamruzzaman, Md Nazirul Islam Sarker, Madallah Alruwaili, Yousef Alhwaiti, Nasser Alshammari, Muhammad Hameed Siddiqi, “Boosting Breast Cancer Detection Using Convolutional Neural Network”, Journal of Healthcare Engineering, vol. 2021, Article ID 5528622, 11 pages (2021). https://doi.org/10.1155/2021/5528622.
[9] Yue, Wenbin & Wang, Zidong & Chen, Hongwei & Payne, Annette & Liu, Xiaohui. Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis. Designs. 2. 13 (2018). 10.3390/designs2020013.
[10] Hamed, Ghada & Marey, Mohammed & Amin, Safaa & Tolba, Mohamed. Deep Learning in Breast Cancer Detection and Classification (2020). 10.1007/978-3-030-44289-7_30.
[11] Buda M, Saha A, Walsh R, et. al. A Data Set and Deep Learning Algorithm for the Detection of Masses and Architectural Distortions in Digital Breast Tomosynthesis Images. JAMA Netw Open. 4(8) : e2119100 (2021). doi:10.1001/jamanetworkopen.2021.19100.
[12] Yassine Laarichi, Mohammed Barmaki. Explicit formulas for computing matrix trigonometric functions. Journal of Interdisciplinary Mathematics 25:8, pages 2321-2331 (2022).
[13] Malihe Niksirat. Intuitionistic Fuzzy Hub Location Problems: Model and Solution Approach. Fuzzy Information and Engineering 14:1, pages 74-83 (2022).
[14] Neetika Bhandari, Payal Pahwa. Evaluating Partitioning Based Clustering Methods for Extended Non-negative Matrix Factorization (NMF). Intelligent Automation & Soft Computing 35 : 2, pages 2043-2055 (2023).
[15] Joseph Thomas Eghwerido, Pelumi E. Oguntunde, Friday Ikechukwu Agu. The Alpha Power Marshall-Olkin-G Distribution: Properties, and Applications. Sankhya A 85:1, pages 172-197 (2023).
[16] Abdalla Rabie, Eslam Hussam, Abdisalam Hassan Muse, Ramy Abdelhamid Aldallal, Amirah Saeed Alharthi, Hassan M. Aljohani, Melike Kaplan. Estimations in a Constant-Stress Partially Accelerated Life Test for Generalized Rayleigh Distribution under Type-II Hybrid Censoring Scheme. Journal of Mathematics, pages 1-9 (2022).
