Transfer Learning Model for Breast Cancer Detection Using Mammograms from Low-Resource Settings
DOI:
https://doi.org/10.33003/fjs-2026-1012-4866Keywords:
Transfer Learning, Breast Cancer Detection, Convolutional Neural Networks, CBIS-DDSM, Mammography, Low-Resource SettingsAbstract
Breast cancer remains a leading cause of cancer death that affects women worldwide, and the burden is felt most acutely in low resource settings where mammography access is scarce, and radiologist coverage is thin. Transfer learning-based deep learning models were evaluated for practical utility in breast cancer screening where imaging resources are restricted. The CBIS-DDSM dataset (Kaggle JPG version) was used for this study, with pathology labels mapped into a binary benign-versus-malignant classification scheme. The dataset consisted of 3,568 full mammograms and 3,461 ROI images from over 1,500 patients after quality control. 5-fold StratifiedGroupKFold cross-validation was used to prevent patient-level data leakage, which causes performance inflation in published studies, and ensured that no patient data combined training and validation datasets. The research evaluated two types of input data which included cropped region-of-interest lesion patches and complete mammogram images. InceptionV3 achieved the highest performance among ROI models by reaching ROC-AUC 0.821 and PR-AUC 0.775 and sensitivity 0.785. The study used full mammograms to evaluate ResNet50 performance which achieved ROC-AUC 0.838 and sensitivity 0.790 results. The study shows that transfer learning acts as a strong base for AI-assisted mammography triage systems in low-resource environments when patient level assessment continues throughout system development.
References
Alzubaidi, L., Zhang, J., Humaidi, A.J. et al. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data 8, 53 (2021). https://doi.org/10.1186/s40537-021-00444-8
Vourtsis, A., & Berg, W. A. (2019). Breast density implications and supplemental screening. European radiology, 29(4), 1762–1777. https://doi.org/10.1007/s00330-018-5668-8
Carriero, A., Groenhoff, L., Vologina, E., Basile, P., & Albera, M. (2024). Deep Learning in Breast Cancer Imaging: State of the Art and Recent Advancements in Early 2024. Diagnostics (Basel, Switzerland), 14(8), 848. https://doi.org/10.3390/diagnostics14080848
Mutar, M. T., Majid, M., Ibrahim, M. J., Obaid, A. H., Alsammarraie, A. Z., Altameemi, E., & Kareem, T. F. (2023). Transfer learning with different modified convolutional neural network models for classifying digital mammograms utilizing Local Dataset. The Gulf journal of oncology, 1(41), 66–71.
Adeleye, D. (2023). Mammography in Nigeria: A rigorous retrospective analysis of one-year utilization in a tertiary hospital. Annals of Breast Cancer Research, 7(1), 1021. https://www.jscimedcentral.com/public/assets/articles/breastcancer-7-1021.pdf
McKinney, S.M., Sieniek, M., Godbole, V. et al. International evaluation of an AI system for breast cancer screening. Nature 577, 89–94 (2020). https://doi.org/10.1038/s41586-019-1799-6
Morid, M. A., Borjali, A., & Del Fiol, G. (2021). A scoping review of transfer learning research on medical image analysis using ImageNet. Computers in biology and medicine, 128, 104115. https://doi.org/10.1016/j.compbiomed.2020.104115
Elewonibi, B., & BeLue, R. (2019). The influence of socio-cultural factors on breast cancer screening behaviors in Lagos, Nigeria. Ethnicity & health, 24(5), 544–559. https://doi.org/10.1080/13557858.2017.1348489
Omisore, A. D., Sutton, E. J., Akinola, R. A., Towoju, A. G., Akhigbe, A., Ebubedike, U. R., Tansley, G., Olasehinde, O., Goyal, A., Akinde, A. O., Alatise, O. I., Mango, V. L., Kingham, T. P., & Knapp, G. C. (2023). Population-Level Access to Breast Cancer Early Detection and Diagnosis in Nigeria. JCO global oncology, 9, e2300093. https://doi.org/10.1200/GO.23.00093
Matta, S., Lamard, M., Zhang, P., Le Guilcher, A., Borderie, L., Cochener, B., & Quellec, G. (2024). A systematic review of generalization research in medical image classification. Computers in Biology and Medicine, 183, 109256. https://doi.org/10.1016/j.compbiomed.2024.109256
Sung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., & Bray, F. (2021). Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: a cancer journal for clinicians, 71(3), 209–249. https://doi.org/10.3322/caac.21660
Nja, G. (2021). Breast Cancer Knowledge and Mammography Uptake among Women Aged 40 Years and Above in Calabar Municipality, Nigeria. Asian Journal of Medicine and Health, 19(8). https://doi.org/10.9734/AJMAH/2021/v19i830351
Luo, L., Wang, X., Lin, Y., Ma, X., Tan, A., Chan, R., Vardhanabhuti, V., Chu, W. C. W., Cheng, K. T., & Chen, H. (2025). Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions. IEEE Reviews in Biomedical Engineering, 18, 130–151. https://doi.org/10.1109/RBME.2024.3357877
Wu, N., Phang, J., Park, J., Shen, Y., Huang, Z., Zorin, M., Jastrzębski, S., Févry, T., Katsnelson, J., Kim, E., Wolfson, S., Parikh, U., Gaddam, S., Lin, L. L. Y., Ho, K., Weinstein, J. D., Reig, B., Gao, Y., Toth, H., Geras, K. J. (2019). Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening. ArXiv:1903.08297.
Olofintuyi, . S. S. (2023). BREAST CANCER DETECTION WITH MACHINE LEARNING APPROACH. FUDMA JOURNAL OF SCIENCES, 7(2), 216-222. https://doi.org/10.33003/fjs-2023-0702-1392
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Copyright (c) 2026 Zinat Abdulkadiri, Muhammad Suleiman, Joshua Abah
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