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Application and Deployment of a Fine-Tuned Pre-trained Deep Model for Breast Cancer Classification
Research Article | Published: 08 November 2025
ICCK Journal of Image Analysis and Processing Volume 1, Issue 4, 2025: 162-171
Volume 1, Issue 4, ICCK Journal of Image Analysis and Processing
Volume 1, Issue 4, 2025
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ICCK Journal of Image Analysis and Processing, Volume 1, Issue 4, 2025: 162-171

Open Access | Research Article | 08 November 2025
Application and Deployment of a Fine-Tuned Pre-trained Deep Model for Breast Cancer Classification
by
Hmidi Alaeddine Hmidi Alaeddine 1,2 *
Lina Tekari Lina Tekari 3
1 Higher Institute of Applied Sciences and Technology of Kasserine, University of Kairouan, Kasserine 1200, Tunisia
2 Laboratory of Electronics and Microelectronics, Faculty of Sciences of Monastir, Monastir University, Monastir 5000, Tunisia
3 Faculty of Medicine, Lucian Blaga University of Sibiu, Sibiu 550024, Romania
* Corresponding Author: Hmidi Alaeddine, [email protected]
DOI: 10.62762/JIAP.2025.421429
Received: 31 August 2025, Accepted: 20 September 2025, Published: 08 November 2025  
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Abstract
Breast cancer remains one of the most significant health challenges, being the second leading cause of death among women worldwide. Early and accurate diagnosis is critical to improving treatment outcomes and increasing survival rates. In this study, we present an innovative application of the WRN-28-2 model, a deep convolutional neural network pre-trained on ImageNet, for the classification of histopathological breast cancer images from the BreakHis dataset. By leveraging transfer learning, the model was fine-tuned to differentiate between benign and malignant cases, achieving a remarkable classification accuracy of 99.16% on the test set. Moreover, the model outperformed existing state-of-the-art techniques on the same dataset. This research highlights the efficiency and adaptability of WRN-28-2 for medical image classification and opens avenues for its deployment in real-world diagnostic scenarios, particularly in resource-constrained environments.
Graphical Abstract
Application and Deployment of a Fine-Tuned Pre-trained Deep Model for Breast Cancer Classification
Keywords
wide ResNet
CNN
breakHis
classification
breast cancer
histopathological image
Data Availability Statement
Data will be made available on request.
Funding
This work was supported without any funding.
Conflicts of Interest
The authors declare no conflicts of interest.
Ethical Approval and Consent to Participate
Not applicable.
References
  1. Youlden, D. R., Cramb, S. M., Dunn, N. A. M., Muller, J. M., Pyke, C. M., & Baade, P. D. (2012). The descriptive epidemiology of female breast cancer: An international comparison of screening, incidence, survival and mortality. Cancer Epidemiology, 36(3), 237–248.
    [CrossRef]   [Google Scholar]
  2. Wang, L. (2017). Early Diagnosis of Breast Cancer. Sensors, 17(7), 1572.
    [CrossRef]   [Google Scholar]
  3. Araújo, T., Aresta, G., Castro, E., Rouco, J., Aguiar, P., Eloy, C., ... & Campilho, A. (2017). Classification of breast cancer histology images using convolutional neural networks. PloS one, 12(6), e0177544.
    [CrossRef]   [Google Scholar]
  4. Mohebian, M. R., Marateb, H. R., Mansourian, M., Mañanas, M. A., & Mokarian, F. (2017). A Hybrid Computer-aided-diagnosis System for Prediction of Breast Cancer Recurrence (HPBCR) Using Optimized Ensemble Learning. Computational and Structural Biotechnology Journal, 15, 75–85.
    [CrossRef]   [Google Scholar]
  5. Wolterink, J. M., Leiner, T., Viergever, M. A., & Išgum, I. (2015, October). Automatic coronary calcium scoring in cardiac CT angiography using convolutional neural networks. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 589-596). Cham: Springer International Publishing.
    [CrossRef]   [Google Scholar]
  6. Pan, Y., Huang, W., Lin, Z., Zhu, W., Zhou, J., Wong, J., & Ding, Z. (2015, August). Brain tumor grading based on neural networks and convolutional neural networks. In 2015 37th annual international conference of the IEEE engineering in medicine and biology society (EMBC) (pp. 699-702). IEEE.
    [CrossRef]   [Google Scholar]
  7. Shen, W., Zhou, M., Yang, F., Yang, C., & Tian, J. (2015, June). Multi-scale convolutional neural networks for lung nodule classification. In International conference on information processing in medical imaging (pp. 588-599). Cham: Springer International Publishing.
    [CrossRef]   [Google Scholar]
  8. Van Ginneken, B., Setio, A. A., Jacobs, C., & Ciompi, F. (2015, April). Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans. In 2015 IEEE 12th International symposium on biomedical imaging (ISBI) (pp. 286-289). IEEE.
    [CrossRef]   [Google Scholar]
  9. Bar, Y., Diamant, I., Wolf, L., Lieberman, S., Konen, E., & Greenspan, H. (2015, April). Chest pathology detection using deep learning with non-medical training. In 2015 IEEE 12th international symposium on biomedical imaging (ISBI) (pp. 294-297). IEEE.
    [CrossRef]   [Google Scholar]
  10. Ciompi, F., de Hoop, B., van Riel, S. J., Chung, K., Scholten, E. T., Oudkerk, M., ... & van Ginneken, B. (2015). Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box. Medical image analysis, 26(1), 195-202.
    [CrossRef]   [Google Scholar]
  11. Schlegl, T., Ofner, J., & Langs, G. (2014, September). Unsupervised pre-training across image domains improves lung tissue classification. In International MICCAI Workshop on Medical Computer Vision (pp. 82-93). Cham: Springer International Publishing.
    [CrossRef]   [Google Scholar]
  12. Carneiro, G., & Nascimento, J. C. (2013). Combining multiple dynamic models and deep learning architectures for tracking the left ventricle endocardium in ultrasound data. IEEE transactions on pattern analysis and machine intelligence, 35(11), 2592-2607.
    [CrossRef]   [Google Scholar]
  13. Li, R., Zhang, W., Suk, H. I., Wang, L., Li, J., Shen, D., & Ji, S. (2014, September). Deep learning based imaging data completion for improved brain disease diagnosis. In International conference on medical image computing and computer-assisted intervention (pp. 305-312). Cham: Springer International Publishing.
    [CrossRef]   [Google Scholar]
  14. Akay, M. F. (2009). Support vector machines combined with feature selection for breast cancer diagnosis. Expert Systems with Applications, 36(2), 3240–3247.
    [CrossRef]   [Google Scholar]
  15. Karabatak, M., & Ince, M. C. (2009). An expert system for detection of breast cancer based on association rules and neural network. Expert Systems with Applications, 36(2), 3465–3469.
    [CrossRef]   [Google Scholar]
  16. Spanhol, F. A., Oliveira, L. S., Petitjean, C., & Heutte, L. (2016). Breast cancer histopathological image classification using Convolutional Neural Networks. In 2016 International Joint Conference on Neural Networks (IJCNN) (pp. 2560–2567).
    [CrossRef]   [Google Scholar]
  17. Ting, F. F., Tan, Y. J., & Sim, K. S. (2019). Convolutional neural network improvement for breast cancer classification. Expert Systems with Applications, 120, 103–115.
    [CrossRef]   [Google Scholar]
  18. Xu, J., Luo, X., Wang, G., Gilmore, H., & Madabhushi, A. (2016). A deep convolutional neural network for segmenting and classifying epithelial and stromal regions in histopathological images. Neurocomputing, 191, 214-223.
    [CrossRef]   [Google Scholar]
  19. Liao, Q., Ding, Y., Jiang, Z. L., Wang, X., Zhang, C., & Zhang, Q. (2019). Multi-task deep convolutional neural network for cancer diagnosis. Neurocomputing, 348, 66-73.
    [CrossRef]   [Google Scholar]
  20. Khan, S., Islam, N., Jan, Z., Din, I. U., & Rodrigues, J. J. C. (2019). A novel deep learning based framework for the detection and classification of breast cancer using transfer learning. Pattern Recognition Letters, 125, 1-6.
    [CrossRef]   [Google Scholar]
  21. Spanhol, F. A., Oliveira, L. S., Petitjean, C., & Heutte, L. (2016). A dataset for breast cancer histopathological image classification. IEEE Transactions on Biomedical Engineering, 63(7), 1455–1462.
    [CrossRef]   [Google Scholar]
  22. Gour, M., Jain, S., & Kumar, T. S. (2020). Residual learning based CNN for breast cancer histopathological image classification. International Journal of Imaging Systems and Technology, 30(3), 621–635.
    [CrossRef]   [Google Scholar]
  23. Dabeer, S., Khan, M. M., & Islam, S. (2019). Cancer diagnosis in histopathological image: CNN based approach. Informatics in Medicine Unlocked, 16, 100231.
    [CrossRef]   [Google Scholar]
  24. Sagar, A. (2019). Convolutional Neural Network for Breast Cancer Classification: Deep Learning for solving the most commonly diagnosed cancer in women. Towards Data Science, A Medium publication sharing concepts, ideas, and codes.
    [Google Scholar]
  25. Kassani, S. H., Kassani, P. H., Wesolowski, M. J., Schneider, K. A., & Deters, R. (2019). Classification of histopathological biopsy images using ensemble of deep learning networks. In Proceedings of the 29th Annual International Conference on Computer Science and Software Engineering (pp. 92–99).
    [Google Scholar]
  26. Adeshina, S. A., Adedigba, A. P., Adeniyi, A. A., & Aibinu, A. M. (2018, November). Breast cancer histopathology image classification with deep convolutional neural networks. In 2018 14th international conference on electronics computer and computation (ICECCO) (pp. 206-212). IEEE.
    [CrossRef]   [Google Scholar]
  27. Han, Z., Wei, B., Zheng, Y., Yin, Y., Li, K., & Li, S. (2017). Breast cancer multi-classification from histopathological images with structured deep learning model. Scientific Reports, 7(1), 4172.
    [CrossRef]   [Google Scholar]
  28. Magee, D., Treanor, D., Crellin, D., Shires, M., Smith, K., Mohee, K., & Quirke, P. (2009). Colour Normalisation in Digital Histopathology Images. In Optical Tissue Image Analysis in Microscopy, Histopathology and Endoscopy: OPTIMHisE (pp. 100-111). MICCAI.
    [Google Scholar]
  29. Zagoruyko, S., & Komodakis, N. (2016). Wide residual networks. In Proceedings of the British Machine Vision Conference (BMVC) (pp. 87.1–87.12).
    [CrossRef]   [Google Scholar]
  30. Al-Haija, Q. A., & Adebanjo, A. (2020, September). Breast cancer diagnosis in histopathological images using ResNet-50 convolutional neural network. In 2020 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS) (pp. 1-7). IEEE.
    [CrossRef]   [Google Scholar]
Cite This Article
APA Style
Alaeddine, H., & Tekari, L. (2025). Application and Deployment of a Fine-Tuned Pre-trained Deep Model for Breast Cancer Classification. ICCK Journal of Image Analysis and Processing, 1(4), 162–171. https://doi.org/10.62762/JIAP.2025.421429
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CC BY Copyright © 2025 by the Author(s). Published by Institute of Central Computation and Knowledge. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
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