Application and Deployment of a Fine-Tuned Pre-trained Deep Model for Breast Cancer Classification

Hmidi Alaeddine; Lina Tekari

doi:10.62762/JIAP.2025.421429

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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

Hmidi Alaeddine 1,2 *

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

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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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TY  - JOUR
AU  - Alaeddine, Hmidi
AU  - Tekari, Lina
PY  - 2025
DA  - 2025/11/08
TI  - Application and Deployment of a Fine-Tuned Pre-trained Deep Model for Breast Cancer Classification
JO  - ICCK Journal of Image Analysis and Processing
T2  - ICCK Journal of Image Analysis and Processing
JF  - ICCK Journal of Image Analysis and Processing
VL  - 1
IS  - 4
SP  - 162
EP  - 171
DO  - 10.62762/JIAP.2025.421429
UR  - https://www.icck.org/article/abs/JIAP.2025.421429
KW  - wide ResNet
KW  - CNN
KW  - breakHis
KW  - classification
KW  - breast cancer
KW  - histopathological image
AB  - 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.
SN  - 3068-6679
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -

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@article{Alaeddine2025Applicatio,
  author = {Hmidi Alaeddine and Lina Tekari},
  title = {Application and Deployment of a Fine-Tuned Pre-trained Deep Model for Breast Cancer Classification},
  journal = {ICCK Journal of Image Analysis and Processing},
  year = {2025},
  volume = {1},
  number = {4},
  pages = {162-171},
  doi = {10.62762/JIAP.2025.421429},
  url = {https://www.icck.org/article/abs/JIAP.2025.421429},
  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.},
  keywords = {wide ResNet, CNN, breakHis, classification, breast cancer, histopathological image},
  issn = {3068-6679},
  publisher = {Institute of Central Computation and Knowledge}
}

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