Design of a Telemedicine System for Classification of Breast Cancer Images

VERSION OF RECORD ONLINE: 12/09/2025

Authors

Corressponding author's email:

nthai@hcmute.edu.vn

DOI:

https://doi.org/10.54644/jte.2025.1969

Keywords:

Telemedicine system, Breast cancer classification, EfficientNet-B7 model, DICOM standard, Protocols for information exchange

Abstract

Breast cancer is one of complex breast lesions. Therefore, accurate diagnosis to determine whether there is cancer disease or not, to determine which stage is a challenge for most doctors. This article proposes a telemedicine system for diagnosing breast cancer disease using EfficientNet-B7 in AI model, in which three image sets of Benign, Malignant and Normal are used. The main points are that, this telemedicine system is designed and calculated suitably so that a DICOM image can be transmitted from the image collected place to a server for classification and diagnosis, in which protocols and storage parts in this system are carefully selected and tested for its efficiency. Furthermore, layers and coefficients of the EfficientNet-B7 model are calculated and selected to increase the classification performance. Thus, the overall system results produced an accuracy of about 89.58%, which is a significant result for a complex and challenging system. Thus, the system can be improved in the future by enhancing the image sets, updating the deep learning network appropriately, and configuring a powerful enough server system.

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Author Biographies

Thanh-Tam Nguyen, Ho Chi Minh City University of Technology and Education, Vietnam

Thanh-Tam Nguyen is a lecturer at School of Biomedical Engineering, International University Vietnam National University - Ho Chi Minh City, Vietnam. He received the B.E. and the M.E degrees in Electronic and Telecommunication Engineering from Ho Chi Minh City University of Technology, Viet Nam, in 2002 and 2004, respectively. He currently working toward the Ph.D. degree in Electronic Engineering at Ho Chi Minh City University of Technology and Education, Viet Nam.

His main research interests include Telemedicine, Machine learning, Telecommunication, Medical Instrumentation.

Email: tamnt.ncs@hcmute.edu.vn. ORCID:  https://orcid.org/0000-0002-7920-1651

Nguyen Thanh Hai, Ho Chi Minh City University of Technology and Education, Vietnam

Thanh-Hai Nguyen is a lecturer and Head of Department of Industrial Electronics and Biomedical Engineering, Faculty of Electrical – Electronics Engineering, HCMC University of Technology and Education, Ho Chi Minh City, Vietnam. He received the B.S. degree in Engineering of Electrical and Electronics from Ho Chi Minh City University of Technology and Education, Vietnam, and the M.E, degree in Electronic-Telecommunication from Ho Chi Minh City University of Technology, Vietnam. He obtained his Ph.D. in Electrical and Electronics Engineering from University of Technology, Sydney, Australia. His main research interests include Signal-Image processing, Biomedical Engineering, Smart wheelchairs, Machine Learning, Artificial Intelligence.

Email: nthai@hcmute.edu.vn. ORCID:  https://orcid.org/0000-0003-3270-6975

Tin-Trung Nguyen, Ho Chi Minh City Oncology Hospital, Vietnam

Tin-Trung Nguyen is Head of Department of Radiology, Ho Chi Minh City Oncology Hospital, Ho Chi Minh City, Vietnam.

His main research interests include Telemedicine, Breast cancer, Lung cancer.

Email: nguyentintrung.dr@gmail.com. ORCID:  https://orcid.org/0000-0002-8894-4289

References

World Health Organization, “Vietnam source: Globocan 2022,” 2022. [Online]. Available: https://gco.iarc.who.int/media/globocan/factsheets/populations/704-viet-nam-fact-sheet.pdf

Vietnamnews, “Bach Mai Hospital leads digital transformation in healthcare,” Vietnamnews, 2025. [Online]. Available: https://vietnamnews.vn/opinion/1693875/bach-mai-hospital-leads-digital-transformation-in-healthcare.html. Accessed: Jun. 18, 2025.

K. N. Ramanto and A. A. Parikesit, “The usage of deep learning algorithm in medical diagnostic of breast cancer,” Malays. J. Fundam. Appl. Sci., vol. 15, 2019, doi: 10.11113/mjfas.v15n2.1231.

Y. Deng et al., “A new framework to reduce doctor’s workload for medical image annotation,” IEEE Access, vol. 7, pp. 107097–107104, 2019, doi: 10.1109/ACCESS.2019.2917932.

N. N. Trung, “Updating and upgrading the features of artificial intelligence (AI) applications in early diagnosis of breast cancer with Korean experts at Thai Binh University of Medicine and Pharmacy Hospital,” Thai Binh Univ. Med. Pharm. Web Portal, 2025. [Online]. Available: https://tbump.edu.vn/tin-hoat-dong/cap-nhat-nang-cap-tinh-nang-cua-ung-dung-tri-tue-nhan-tao-ai-trong-chan-doan-som-ung-thu-vu-cung-cac-chuyen-gia-han-quoc-tai-benh-vien-dai-hoc-y-thai-binh-298.html. Accessed: Jun. 16, 2025.

S. A. A. Karim, U. H. Mohamad, and N. E. N. Puteri, “Discovery of interpretable patterns of breast cancer diagnosis via class association rule mining (CARM) with SHAP-based explainable AI (XAI),” Malays. J. Fundam. Appl. Sci., vol. 21, 2025, doi: 10.11113/mjfas.v21n3.3792.

J. Yan et al., “Diagnosis and treatment of breast cancer in the precision medicine era,” in Precision Medicine, T. Huang, Ed. New York, NY, USA: Springer, 2020, pp. 53–61, doi: 10.1007/978-1-0716-0904-0_5.

K. K. Evans, R. L. Birdwell, and J. M. Wolfe, “If you don’t find it often, you often don’t find it: why some cancers are missed in breast cancer screening,” PLoS One, vol. 8, 2013, Art. no. e64366, doi: 10.1371/journal.pone.0064366.

M. Issaiy, D. Zarei, and A. Saghazadeh, “Artificial intelligence and acute appendicitis: A systematic review of diagnostic and prognostic models,” World J. Emerg. Surg., vol. 18, 2023, Art. no. 59, doi: 10.1186/s13017-023-00527-2.

C. Gupta et al., “Applying YOLOv6 as an ensemble federated learning framework to classify breast cancer pathology images,” Sci. Rep., vol. 15, 2025, Art. no. 3769, doi: 10.1038/s41598-024-80187-7.

A. Kapoor, P. Nambisan, and E. Baker, “Mobile applications for breast cancer survivorship and self-management: A systematic review,” Health Informatics J., vol. 26, pp. 2892–2905, 2020, doi: 10.1177/1460458220950853.

J. V. Johansson and E. Engström, “‘Humans think outside the pixels’ – Radiologists’ perceptions of using artificial intelligence for breast cancer detection in mammography screening in a clinical setting,” Health Informatics J., vol. 30, 2024, Art. no. 14604582241275020, doi: 10.1177/14604582241275020.

N. Zhou et al., “Concordance study between IBM Watson for Oncology and clinical practice for patients with cancer in China,” Oncologist, vol. 24, pp. 812–819, 2019, doi: 10.1634/theoncologist.2018-0255.

Y. Shen et al., “Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams,” MedRxiv, 2021, doi: 10.1101/2021.04.28.21256203.

M. Tan and Q. V. Le, “EfficientNet: Rethinking model scaling for convolutional neural networks,” 2020. [Online]. Available: https://arxiv.org/abs/1905.11946

R. Raza et al., “Lung-EffNet: Lung cancer classification using EfficientNet from CT-scan images,” Eng. Appl. Artif. Intell., vol. 126, 2023, Art. no. 106902, doi: 10.1016/j.engappai.2023.106902.

A. Abdelrahman and S. Viriri, “EfficientNet family U-Net models for deep learning semantic segmentation of kidney tumors on CT images,” Front. Comput. Sci., vol. 5, 2023, doi: 10.3389/fcomp.2023.1235622.

Y. Sun, Z. Zhu, and B. Honarvar Shakibaei Asli, “Automated classification and segmentation and feature extraction from breast imaging data,” Electronics, vol. 13, 2024, doi: 10.3390/electronics13193814.

VinDr, “VinDr – Solutions for medical data,” [Online]. Available: https://vindr.ai/. Accessed: Aug. 1, 2025.

K. Lang et al., “Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial (MASAI): a clinical safety analysis of a randomised, controlled, non-inferiority, single-blinded screening,” Lancet Oncol., vol. 24, pp. 936–944, 2023, doi: 10.1016/S1470-2045(23)00298-X.

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Published

12-09-2025

How to Cite

Nguyen, T.-T., Thanh Hai, N., & Nguyen, T.-T. (2025). Design of a Telemedicine System for Classification of Breast Cancer Images: VERSION OF RECORD ONLINE: 12/09/2025. Journal of Technical Education Science. https://doi.org/10.54644/jte.2025.1969