Breast Cancer Image Classification Using Multi-Modal Datasets: A Comprehensive Review

Authors

  • Sejal Parmar Department of Computer Engineering (Software Engineering), Ipcowala Institute of Engineering & Technology, Dharmaj, Gujarat Technological University, Gujarat, India Author
  • Dr. Padiya Swity R Professor, Department of Computer Engineering, Ipcowala Institute of Engineering & Technology, Dharmaj, Gujarat Technological University, Gujarat, India Author
  • Prof. Patel Ketankumar Assistant Professor, Department of Computer Engineering, Ipcowala Institute of Engineering & Technology, Dharmaj, Gujarat Technological University, Gujarat, India Author

DOI:

https://doi.org/10.32628/CSEIT261217

Keywords:

Breast Cancer, Deep Learning, Image Classification, Histopathology, Medical Datasets

Abstract

Breast cancer is one of the most frequently diagnosed malignancies worldwide and remains a leading cause of cancer-related mortality among women. Early and accurate detection significantly improves survival rates and treatment outcomes. Recent advances in artificial intelligence, particularly deep learning, have transformed breast cancer diagnosis through automated image classification. This review presents a comprehensive analysis of breast cancer image classification techniques across multiple dataset modalities, including histopathological images, mammography scans, cytopathology images, and multi-resolution datasets. Traditional machine learning methods and modern deep learning architectures are critically examined with respect to feature extraction, dataset characteristics, and classification performance. Special attention is given to transfer learning, hybrid models, class imbalance handling, and magnification variability. The review also highlights key research findings, limitations, and open challenges hindering clinical adoption. By synthesizing recent state-of-the-art studies, this paper provides valuable insights into current trends and future directions for developing robust, accurate, and clinically deployable breast cancer diagnostic systems.

Downloads

Download data is not yet available.

References

S. Zarif, H. Abdulkader, I. Elaraby, A. Alharbi, W. S. Elkilani, and P. Pławiak, “Using hybrid pre-trained models for breast cancer detection,” PLoS ONE, vol. 19, no. 1 January, 2024, doi: 10.1371/journal.pone.0296912. DOI: https://doi.org/10.1371/journal.pone.0296912

S. P. Jakkaladiki and F. Maly, “Integrating hybrid transfer learning with attention-enhanced deep learning models to improve breast cancer diagnosis,” PeerJ Computer Science, vol. 10, 2024, doi: 10.7717/peerj-cs.1850. DOI: https://doi.org/10.7717/peerj-cs.1850

S. Taheri, Z. Golrizkhatami, A. A. Basabrain, and M. S. Hazzazi, “A Comprehensive Study on Classification of Breast Cancer Histopathological Images: Binary Versus Multi-Category and Magnification-Specific Versus Magnification-Independent,” IEEE Access, vol. 12, no. April, pp. 50431–50443, 2024, doi: 10.1109/ACCESS.2024.3386355. DOI: https://doi.org/10.1109/ACCESS.2024.3386355

D. Karuppasamy, A. Abdesselam, R. Hedjam, H. zidoum, and M. Al-Bahri, “Feed-forward networks using logistic regression and support vector machine for whole-slide breast cancer histopathology image classification,” Intelligence-Based Medicine, vol. 9, no. March 2023, p. 100126, 2024, doi: 10.1016/j.ibmed.2023.100126. DOI: https://doi.org/10.1016/j.ibmed.2023.100126

J. Potsangbam and S. Shuleenda Devi, “Classification of Breast Cancer Histopathological Images Using Transfer Learning with DenseNet121,” Procedia Computer Science, vol. 235, pp. 1990–1997, 2024, doi: 10.1016/j.procs.2024.04.188. DOI: https://doi.org/10.1016/j.procs.2024.04.188

R. B. Eshun, M. Bikdash, and A. K. M. K. Islam, “A deep convolutional neural network for the classification of imbalanced breast cancer dataset,” Healthcare Analytics, vol. 5, no. March, p. 100330, 2024, doi: 10.1016/j.health.2024.100330. DOI: https://doi.org/10.1016/j.health.2024.100330

M. Xiao, Y. Li, X. Yan, M. Gao, and W. Wang, “Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an example,” arxiv, vol. 12, 2024, doi: https://doi.org/10.48550/arXiv.2404.08279. DOI: https://doi.org/10.1145/3653946.3653968

W. Wang, M. Gao, M. Xiao, X. Yan, and Y. Li, “Breast Cancer Image Classification Method Based on Deep Transfer Learning,” arxiv, vol. 14, 2024, doi: https://doi.org/10.48550/arXiv.2404.09226. DOI: https://doi.org/10.1145/3700906.3700937

M. M. Dhas and N. S. Singh, “Breast Cancer Diagnosis Using Majority Voting Ensemble Classifier Approach,” JOURNAL OF SOFT COMPUTING AND DATA MINING, vol. 5, no. 1, pp. 152–169, 2024, doi: https://doi.org/10.30880/jscdm.2024.05.01.013 Article. DOI: https://doi.org/10.30880/jscdm.2024.05.01.013

Al Tawil, A. Shaban, and L. Almazaydeh, “A comparative analysis of convolutional neural networks for breast cancer prediction,” International Journal of Electrical and Computer Engineering, vol. 14, no. 3, pp. 3406–3414, 2024, doi: 10.11591/ijece.v14i3.pp3406-3414. DOI: https://doi.org/10.11591/ijece.v14i3.pp3406-3414

T. A. Toma et al., “Breast Cancer Detection Based on Simplified Deep Learning Technique With Histopathological Image Using BreaKHis Database,” Radio Science, vol. 58, no. 11, 2023, doi: 10.1029/2023RS007761. DOI: https://doi.org/10.1029/2023RS007761

İ. Sayın et al., “Comparative Analysis of Deep Learning Architectures for Breast Cancer Diagnosis Using the BreaKHis Dataset,” IEEE, 2023, doi: https://doi.org/10.48550/arXiv.2309.01007.

J. R. Leow, W. H. Khoh, Y. H. Pang, and H. Y. Yap, “Breast cancer classification with histopathological image based on machine learning,” International Journal of Electrical and Computer Engineering, vol. 13, no. 5, pp. 5885–5897, 2023, doi: 10.11591/ijece.v13i5.pp5885-5897. DOI: https://doi.org/10.11591/ijece.v13i5.pp5885-5897

Maleki, M. Raahemi, and H. Nasiri, “Breast cancer diagnosis from histopathology images using deep neural network and XGBoost,” Biomedical Signal Processing and Control, vol. 86, pp. 1–12, 2023, doi: 10.1016/j.bspc.2023.105152. DOI: https://doi.org/10.1016/j.bspc.2023.105152

Akram et al., “Recognizing Breast Cancer Using Edge-Weighted Texture Features of Histopathology Images,” Computers, Materials and Continua, vol. 77, no. 1, pp. 1081–1101, 2023, doi: 10.32604/cmc.2023.041558. DOI: https://doi.org/10.32604/cmc.2023.041558

Bagchi, P. Pramanik, and R. Sarkar, “A Multi-Stage Approach to Breast Cancer Classification Using Histopathology Images,” Diagnostics, vol. 13, no. 1, 2023, doi: 10.3390/diagnostics13010126. DOI: https://doi.org/10.3390/diagnostics13010126

Y. Zhou, C. Zhang, and S. Gao, “Breast Cancer Classification From Histopathological Images Using Resolution Adaptive Network,” IEEE Access, vol. 10, pp. 35977–35991, 2022, doi: 10.1109/ACCESS.2022.3163822. DOI: https://doi.org/10.1109/ACCESS.2022.3163822

M. Liu et al., “A Deep Learning Method for Breast Cancer Classification in the Pathology Images,” IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 10, pp. 5025–5032, 2022, doi: 10.1109/JBHI.2022.3187765. DOI: https://doi.org/10.1109/JBHI.2022.3187765

N. Ahmad, S. Asghar, and S. A. Gillani, “Transfer learning-assisted multi-resolution breast cancer histopathological images classification,” Visual Computer, vol. 38, no. 8, pp. 2751–2770, 2022, doi: 10.1007/s00371-021-02153-y. DOI: https://doi.org/10.1007/s00371-021-02153-y

F. A. Spanhol et al., “Breast Cancer Histopathological Image Classification using Convolutional Neural Networks,” International Joint Conference on Neural Networks (IJCNN 2016), vol. 2017-Janua, pp. 1868–1873, 2017, doi: 10.1109/IJCNN.2016.7727519. DOI: https://doi.org/10.1109/IJCNN.2016.7727519

Downloads

Published

10-01-2026

Issue

Vol. 12 No. 1 (2026): January-February

Section

Research Articles

License

Copyright (c) 2026 International Journal of Scientific Research in Computer Science, Engineering and Information Technology

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Sejal Parmar, Dr. Padiya Swity R, and Prof. Patel Ketankumar, “Breast Cancer Image Classification Using Multi-Modal Datasets: A Comprehensive Review”, Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol, vol. 12, no. 1, pp. 128–133, Jan. 2026, doi: 10.32628/CSEIT261217.