Computer-Aided Diagnosis Models for Breast Cancer Detection Decision Support Systems
DOI:
https://doi.org/10.47839/ijc.24.1.3878Keywords:
breast cancer, image processing, neural networks, machine learning, computer-aided diagnosisAbstract
Computer-aided diagnosis (CADx) technology has demonstrated enhanced efficacy in breast cancer (BC) detection, which is particularly crucial during primary care physicians' initial evaluation of patients. The work aims to develop novel, user-friendly auxiliary computer aids accessible directly to frontline medical practitioners, eliminating the need for costly computer systems. The scientific novelty lies in the fact that we have devised a non-relational database (DB) of factual data designed to house the results of patient studies, which can be harnessed for machine learning in computer-aided BC diagnosis systems. The DB encompasses a heterogeneous vector of primary measurements (metadata, DICOM standard files, alongside other images and data) for each patient, facilitating the construction of a neural network for tumor recognition and preliminary classification. We have populated the database with new, region-specific data pertinent to women in Ukraine amidst severe stress induced by the ongoing war. Additionally, we have developed a new system for concurrent monitoring of ultrasound, computed tomography, and mammography results, complemented by a decision support system for simultaneous cross-verification of neoplasm diagnoses based on density and spatial correlation.
References
R. Bayareh-Mancilla, L. A. Medina-Ramos, A. Toriz-Vázquez, Y. M. Hernández-Rodríguez, and O. E. Cigarroa-Mayorga, “Automated computer-assisted medical decision-making system based on morphological shape and skin thickness analysis for asymmetry detection in mammographic images,” Diagnostics, vol. 13, no. 22, p. 3440, 2025. https://doi.org/10.3390/diagnostics13223440
N. M. Hassan, S. Hamad, and K. Mahar, “Mammogram breast cancer CAD systems for mass detection and classification: a review,” Multimedia Tools and Applications, vol. 81, pp. 20043–20075, 2022. https://doi.org/10.1007/s11042-022-12332-1
K. S. Priyanka, “A review paper on breast cancer detection using deep learning,” IOP Conference Series: Materials Science and Engineering, vol. 1022, p. 012071, 2021. https://doi.org/10.1088/1757-899X/1022/1/012071
S.M. McKinney, M. Sieniek, V. Godbole, et al., “International evaluation of an AI system for breast cancer screening,” Nature, vol. 577, pp. 89–94, 2020. https://doi.org/10.1038/s41586-019-1799-6
“The mini-MIAS database of mammograms,” PEIPA, the Pilot European Image Processing Archive. 2025. [Online]. Available at: http://peipa.essex.ac.uk/info/mias.html
L. Tsochatzidis, L. Costaridou, and I. Pratikakis, “Deep learning for breast cancer diagnosis from mammograms – A comparative study,” Journal of Imaging, vol. 5, no. 3, p. 37, 2019. https://doi.org/10.3390/jimaging5030037
L. Wang, “Mammography with deep learning for breast cancer detection,” Frontiers in Oncology, vol. 14, p. 1281922, 2024. https://doi.org/10.3389/fonc.2024.1281922
L. Wang, B. Platel, T. Ivanovskaya, M. Harz and H. K. Hahn, “Fully automatic breast segmentation in 3D breast MRI,” Proceedings of the 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), Barcelona, Spain, 2012, pp. 1024-1027. https://doi.org/10.1109/ISBI.2012.6235732
V. Gnatushenko, “The use of geometrical methods in multispectral image processing,” Journal of Automation and Information Sciences, vol. 35, no. 12, pp. 1–8, 2003. https://doi.org/10.1615/JAutomatInfScien.v35.i12.10
“Breast cancer. UCI Machine Learning Repository,” Home Page. Accessed: Jan. 25, 2025. https://doi.org/10.24432/C51P4M
“USF Digital Mammography Home Page,” College of Engineering, University of South Florida. [Online]. Available at: http://www.eng.usf.edu/cvprg/mammography/database.html
“Facility Oncology Registry Data Standards (FORDS): Revised for 2016,” ACS. [Online]. Available at: https://www.facs.org/quality-programs/cancer-programs/national-cancer-database/ncdb-call-for-data/fordsmanual/
“Breast Imaging Reporting & Data System (BI-RADS),” American College of Radiology. [Online]. Available: https://www.acr.org/Clinical-Resources/Reporting-and-Data-Systems/Bi-Rads
M. Tahmooresi, A. Afshar, B. Bashari Rad, K. B. Nowshath, and M. A. Bamiah, “Early detection of breast cancer using machine learning techniques,” Journal of Telecommunication, Electronic and Computer Engineering (JTEC), vol. 10, no. 3-2, pp. 21–27, 2018. https://jtec.utem.edu.my/jtec/article/view/4706
“Mammographic Image Analysis Homepage – Databases,” Mammographic Image Analysis Homepage. [Online]. Available at: https://www.mammoimage.org/databases/
B. Meduri et al., “Cosmetic results and side effects of accelerated partial-breast irradiation versus whole-breast irradiation for low-risk invasive carcinoma of the breast: The randomized phase III IRMA trial,” Journal of Clinical Oncology, vol. 41, no. 12, pp. 2201-2210, 2023. https://doi.org/10.1200/jco.22.01485
G. Chuyko, O. Yaremchuk, “Handling the breast cancer recurrence data for a more reliable forecast,” Computer Systems and Information Technologies, no. 4, pp. 10-15, 2023. (in Ukrainian). https://doi.org/10.31891/csit-2023-4-2
S. Jodogne, “The Orthanc ecosystem for medical imaging,” Journal of Digital Imaging, vol. 31, no. 3, pp. 341–352, 2018. https://doi.org/10.1007/s10278-018-0082-y
D. Kreuzberger, N. Kühl, and S. Hirschl, “Machine learning operations (MLOps): Overview, definition, and architecture,” IEEE Access, p. 1, 2023. https://doi.org/10.1109/ACCESS.2023.3262138
I. Adusei, O. Kuljaca, and K. Agyepong, “Intelligent mammography database management system for a computer aided breast cancer detection and diagnosis,” International Journal of Managing Information Technology, vol. 2, no. 2, pp. 1–13, 2010. http://dx.doi.org/10.5121/ijmit.2010.2201
A. Fox, D. Patterson, Engineering Software as a Service: An Agile Approach Using Cloud Computing, Second edition, Pogo Press, 2021, 430 p. https://www.amazon.com/Engineering-Software-As-Service-Computing/dp/1735233803
C. He, X. Fan, and Y. Li, “Toward ubiquitous healthcare services with a novel efficient cloud platform,” IEEE Transactions on Biomedical Engineering, vol. 60, no. 1, pp. 230–234, 2013. https://doi.org/10.1109/TBME.2012.2222404
Y.-R. Lin and S.-C. Lo, “The design of a cloud and mobile healthcare system,” International Journal of Communications, Network and System Sciences, vol. 09, no. 05, pp. 209–218, 2016. https://doi.org/10.4236/ijcns.2016.95020
M. Rahman and N. Alpaslan, “A decision support system (DSS) for breast cancer detection based on invariant feature extraction, classification, and retrieval of masses of mammographic images,” Medical Imaging and Image-Guided Interventions. IntechOpen, Jul. 24, 2019. http://dx.doi.org/10.5772/intechopen.81119
V.V. Hnatushenko, О.V. Spirintseva, V.V. Spirintsev, O.V. Kravets, D.V. Spirintsev. “Homomorphic filtering in digital multichannel image processing,” Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, no. 3, pp. 118-124, 2023. https://doi.org/10.33271/nvngu/2023-3/118
Y.B. Olevska, V.I. Olevskyi, O.V. Olevskyi, Hnatushenko V.V., “Raster image processing using 2D Padé-type approximations,” Journal of Physics: Conference Series, vol. 2675, p. 012015, 2023. https://doi.org/10.1088/1742-6596/2675/1/012015
V. Hnatushenko, P. Kogut, M. Uvarov, “On flexible co-registration of optical and SAR satellite images,” In: S. Babichev, V. Lytvynenko, W. Wójcik, S. Vyshemyrskaya (Eds), Lecture Notes in Computational Intelligence and Decision Making. ISDMCI, Springer, Cham. Advances in Intelligent Systems and Computing vol. 1246, 2020, pp. 515-534. https://doi.org/10.1007/978-3-030-54215-3_33
T.Münch, System Architecture Design and Platform Development Strategies, 2022, 197 p. https://doi.org/10.1007/978-3-030-97695-8
I. C. Moreira, I. Amaral, I. Domingues, A. Cardoso, M. J. Cardoso, J. S. Cardoso, “INbreast: Toward a full-field digital mammographic database,” Academic Radiology, vol. 19, issue 2, pp. 236-248, 2012. https://doi.org/10.1016/j.acra.2011.09.014
D.K. Gurmessa, W. Jimma, “Explainable machine learning for breast cancer diagnosis from mammography and ultrasound images: a systematic review,” BMJ Health & Care Informatics (BMJ HCI), vol. 31, no. 1, Article ID e100954, 2024. https://doi.org/10.1136/bmjhci-2023-100954
R. Kehm, J. Walter, A. Pereira, M. White, S. Oskar, K. Michels, J. Shepherd, L. Lilge, M. Terry, “A comparison of various methods for measuring breast density and breast tissue composition in adolescent girls and women,” Scientific Reports, vol. 12, p. 13547, 2022. https://doi.org/10.1038/s41598-022-17800-0
W. E. Fathy, A. S. Ghoneim, “A deep learning approach for breast cancer mass detection,” International Journal of Advanced Computer Science and Applications, vol. 10, no. 1, pp. 175-182, 2019. https://doi.org/10.14569/IJACSA.2019.0100123
N. Mahajan, E. Holzwanger, J. G. Brown, T. M. Berzin, “Deploying automated machine learning for computer vision projects: a brief introduction for endoscopists,” VideoGIE, vol. 8, pp. 249-251, 2023. https://doi.org/10.1016/j.vgie.2023.02.012
R. Peleshchak, V. Lytvyn, I. Peleshchak, O. Veres, “Neural network architecture with oscillatory synaptic blocks for signal processing of spatially spaced multiband radar complex,” Lecture Notes in Electrical Engineering, vol. 965, pp. 367–374, 2023. DOI: 10.1007/978-3-031-24963-1_21
I.K. Sandhu, M. Nair, R.P. Aharwal, S.S. Sandhu, P. Nagar, “Diagnosis of cancer using artificial neural network and cloud computing approach,” World Journal of Pharmacy and Pharmaceutical Sciences, vol. 3, issue 6, pp. 1533-1548, 2014.
M. Copperwaite, C. Leifer, Learning Flask Framework. Build Dynamic, Data Driven Websites and Modern Web Applications with Flask, Packt Publishing, 2015, 250 p. ISBN 978-1-78398-336-0
A. Freeman, Pro React 16, Apress Berkeley, CA, 2019, 745 p. http://dx.doi.org/10.1007/978-1-4842-4451-7
E. Ziegler, T. Urban, D. Brown, J. Petts, S.D. Pieper, R. Lewis, C. Hafey, G. J. Harris, “Open health imaging foundation viewer: an extensible open-source framework for building web-based imaging applications to support cancer research,” JCO Clinical Cancer Informatics, vol. 4, pp. 336-345, 2020. https://doi.org/10.1200/CCI.19.00131
B. Rusyn et al., “Rethinking deep CNN training: A novel approach for quality-aware dataset optimization,” IEEE Access, vol. 12, pp. 137427-137438, 2024, http://dx.doi.org/10.1109/ACCESS.2024.3414651.
A. Sachenko, V. Vysotska, S. Vladov, V. Vasylenko, M. Dobrowolski, “Hybrid method for restoring missing sensor data with adaptive control based on neuro-fuzzy networks”. CEUR Workshop Proceedings, 2025, 3899, pp. 186–200. https://ceur-ws.org/Vol-3899/paper17.pdf
Downloads
Published
How to Cite
Issue
Section
License
International Journal of Computing is an open access journal. Authors who publish with this journal agree to the following terms:• Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
• Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
• Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
