CNN-Based Automated Detection of Metastatic Cancer in Histopathology Images
Received: 15 March 2025 | Revised: 26 April 2025 | Accepted: 8 May 2025 | Online: 26 May 2025
Corresponding author: Omaia Al-Omari
Abstract
Breast cancer remains one of the leading causes of cancer-related mortality, underscoring the critical need for accurate and efficient diagnostic solutions. This study presents an enhanced Deep Learning (DL) framework for the classification of breast cancer histopathological images, integrating both advanced Convolutional Neural Network (CNN) architectures and explainability techniques. The proposed approach utilizes the publicly available BreaKHis dataset, which contains over 7,900 histopathological images of benign and malignant breast tumors captured at varying magnification levels (40×, 100×, 200×, and 400×). An EfficientNetB3-based CNN is employed for automated feature extraction and classification, addressing the limitations of traditional Machine Learning (ML) methods that rely on handcrafted features and typically suffer from reduced generalizability. The proposed model significantly outperforms conventional classifiers, including Random Forest (RF) (74.56%), Support Vector Machines (SVM) (77.34%), and k-Nearest Neighbors (k-NN) (69.67%), by achieving a test accuracy of 92.33% on the BreaKHis dataset. To enhance model transparency and clinical relevance, Gradient-weighted Class Activation Mapping (Grad-CAM) is incorporated, which accurately localizes malignant regions in over 95% of test samples, offering visual interpretability of model predictions. Additionally, dimensionality reduction techniques, such as t-distributed Stochastic Neighbor Embedding (t-SNE) and Principal Component Analysis (PCA) are employed to analyze the feature space. These analyses reveal improved separability between benign and malignant clusters, further validating the effectiveness of the learned representations. The results of this work demonstrate the transformative potential of DL, particularly EfficientNet-based CNN architectures, in delivering both high diagnostic accuracy and interpretability, paving the way for more reliable and explainable Artificial Intelligence (AI)-assisted diagnostic systems in histopathology.
Keywords:
breast cancer histopathology, DL, EfficientNetB3, explainable AI (Grad-CAM), image classificationDownloads
References
V. Kumar, A. K. Abbas, and J. C. Aster, Robbins and Cotran Pathologic Basis of Disease, 9th ed. London: Elsevier Health Sciences, 2015.
G. Litjens et al., "A survey on deep learning in medical image analysis," Medical Image Analysis, vol. 42, pp. 60–88, Dec. 2017. DOI: https://doi.org/10.1016/j.media.2017.07.005
A. Janowczyk and A. Madabhushi, "Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases," Journal of Pathology Informatics, vol. 7, no. 1, Jan. 2016, Art. no. 29. DOI: https://doi.org/10.4103/2153-3539.186902
D. Komura and S. Ishikawa, "Machine Learning Methods for Histopathological Image Analysis," Computational and Structural Biotechnology Journal, vol. 16, pp. 34–42, 2018. DOI: https://doi.org/10.1016/j.csbj.2018.01.001
G. Campanella et al., "Clinical-grade computational pathology using weakly supervised deep learning on whole slide images," Nature Medicine, vol. 25, no. 8, pp. 1301–1309, Aug. 2019. DOI: https://doi.org/10.1038/s41591-019-0508-1
D. Wang, A. Khosla, R. Gargeya, H. Irshad, and A. H. Beck, "Deep Learning for Identifying Metastatic Breast Cancer." arXiv, Jun. 18, 2016.
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, "Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization," International Journal of Computer Vision, vol. 128, no. 2, pp. 336–359, Feb. 2020. DOI: https://doi.org/10.1007/s11263-019-01228-7
F. A. Spanhol, L. S. Oliveira, C. Petitjean, and L. Heutte, "A Dataset for Breast Cancer Histopathological Image Classification," IEEE Transactions on Biomedical Engineering, vol. 63, no. 7, pp. 1455–1462, Jul. 2016. DOI: https://doi.org/10.1109/TBME.2015.2496264
M. Veta et al., "Assessment of algorithms for mitosis detection in breast cancer histopathology images," Medical Image Analysis, vol. 20, no. 1, pp. 237–248, Feb. 2015. DOI: https://doi.org/10.1016/j.media.2014.11.010
E. Reinhard, M. Adhikhmin, B. Gooch, and P. Shirley, "Color transfer between images," IEEE Computer Graphics and Applications, vol. 21, no. 4, pp. 34–41, Aug. 2001. DOI: https://doi.org/10.1109/38.946629
B. E. Bejnordi et al., "Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer," JAMA, vol. 318, no. 22, Dec. 2017, Art. no. 2199. DOI: https://doi.org/10.1001/jama.2017.14580
T. Araújo et al., "Classification of breast cancer histology images using Convolutional Neural Networks," PLoS One, vol. 12, no. 6, Jun. 2017, Art. no. e0177544. DOI: https://doi.org/10.1371/journal.pone.0177544
H. Wang et al., "Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features," Journal of Medical Imaging, vol. 1, no. 3, Oct. 2014, Art. no. 034003. DOI: https://doi.org/10.1117/1.JMI.1.3.034003
M. Ilse, J. M. Tomczak, and M. Welling, "Attention-based Deep Multiple Instance Learning." arXiv, 2018.
V. S. Krushnasamy, O. Al-Omari, A. Sundaram, I. K. Varghese, E. Muniyandy, and M. V. A. L. N. Rao, "LiDAR-Based Climate Change Imaging in Geoscience Using Spatio Extreme Fuzzy Gradient Model," Remote Sensing in Earth Systems Sciences, Jan. 2025. DOI: https://doi.org/10.1007/s41976-025-00197-5
L. S. Geetha et al., "Challenges and Solutions in Agile Software Development: A Managerial Perspective on Implementation Practices," International Journal of Advanced Computer Science and Applications, vol. 16, no. 3, 2025. DOI: https://doi.org/10.14569/IJACSA.2025.0160374
A. Dosovitskiy et al., "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale." arXiv, 2020.
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, "A Simple Framework for Contrastive Learning of Visual Representations." arXiv, 2020.
X. Li, Y. Gu, N. Dvornek, L. H. Staib, P. Ventola, and J. S. Duncan, "Multi-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results," Medical Image Analysis, vol. 65, Oct. 2020, Art. no. 101765. DOI: https://doi.org/10.1016/j.media.2020.101765
Y. Ektefaie et al., "Integrative multiomics-histopathology analysis for breast cancer classification," npj Breast Cancer, vol. 7, no. 1, Nov. 2021, Art. no. 147. DOI: https://doi.org/10.1038/s41523-021-00357-y
S. M. Fati and M. Alenezi, "Transforming Application Development With Serverless Computing:," International Journal of Cloud Applications and Computing, vol. 14, no. 1, pp. 1–16, Dec. 2024. DOI: https://doi.org/10.4018/IJCAC.365288
O. Al-Omari, A. Alyousef, S. Fati, F. Shannaq, and A. Omari, "Governance and Ethical Frameworks for AI Integration in Higher Education: Enhancing Personalized Learning and Legal Compliance," Journal of Ecohumanism, vol. 4, no. 2, Jan. 2025. DOI: https://doi.org/10.62754/joe.v4i2.5781
A. Alyousef and O. Al-Omari, "Artificial Intelligence in Healthcare: Bridging Innovation and Regulation," Journal of Ecohumanism, vol. 3, no. 8, Jan. 2025. DOI: https://doi.org/10.62754/joe.v3i8.5673
A. Rehman, M. Mujahid, A. Elyassih, B. AlGhofaily, and S. A. O. Bahaj, "Comprehensive Review and Analysis on Facial Emotion Recognition: Performance Insights into Deep and Traditional Learning with Current Updates and Challenges," Computers, Materials & Continua, vol. 82, no. 1, pp. 41–72, 2025. DOI: https://doi.org/10.32604/cmc.2024.058036
N. A. Semary, W. Ahmed, K. Amin, P. Pławiak, and M. Hammad, "Improving sentiment classification using a RoBERTa-based hybrid model," Frontiers in Human Neuroscience, vol. 17, Dec. 2023, Art. no. 1292010. DOI: https://doi.org/10.3389/fnhum.2023.1292010
A. Q. A. Hassan, B. B. Al-onazi, M. Maashi, A. A. Darem, I. Abunadi, and A. Mahmud, "Enhancing extractive text summarization using natural language processing with an optimal deep learning model," AIMS Mathematics, vol. 9, no. 5, pp. 12588–12609, 2024. DOI: https://doi.org/10.3934/math.2024616
A. Jabbar, S. Iqbal, M. I. Tamimy, A. Rehman, S. A. Bahaj, and T. Saba, "An Analytical Analysis of Text Stemming Methodologies in Information Retrieval and Natural Language Processing Systems," IEEE Access, vol. 11, pp. 133681–133702, 2023. DOI: https://doi.org/10.1109/ACCESS.2023.3332710
M. S. Al Huda, T. E. Shrestha, A. Hossain, N. B. Sharif, M. A. Ali, and T. I. Erdei, "DeepMelaNet: Advancing Melanoma Stage Classification in Skin Cancer Diagnosis," Engineering, Technology & Applied Science Research, vol. 15, no. 1, pp. 19627–19635, Feb. 2025. DOI: https://doi.org/10.48084/etasr.8336
A. Ammar, A. Koubaa, B. Benjdira, O. Nacar, and S. Sibaee, "Prediction of Arabic Legal Rulings Using Large Language Models," Electronics, vol. 13, no. 4, Feb. 2024, Art. no. 764. DOI: https://doi.org/10.3390/electronics13040764
BreakHis: Breast Cancer Histopathological Image Classification. (2016), F. A. Spanhol, L. S. Oliveira, C. Petitjean, L. Heutte, C. E. Pokes, F. Negretti and F. S. Rechia. [Online]. Available: http://www.inf.ufpr.br/vri/databases/BreaKHis_v1.tar.gz.
Downloads
How to Cite
License
Copyright (c) 2025 Omaia Al-Omari, Omar Alkhatib, Tariq Al-Omari
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the 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) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
