Keywords:-
Article Content:-
Abstract
Breast cancer remains the most frequently diagnosed cancer and a leading cause of cancer death among women worldwide, so tools that support early and accurate detection are urgently needed. This review synthesizes a pool of one hundred studies, largely published between 2023 and 2026, on deep learning, ensemble learning, and optimization algorithms for breast cancer detection across mammography, ultrasound, magnetic resonance imaging, and histopathology. Relevant studies were identified from major scientific databases using search terms combining breast cancer, deep learning, convolutional neural networks, ensemble learning, metaheuristic optimization, and adversarial robustness, then organized by architecture, ensemble strategy, and outcome. Convolutional Neural Networks such as VGG16/19, ResNet50, DenseNet121, EfficientNet, Xception, and MobileNet, typically adapted through transfer learning, consistently exceed 90% classification accuracy on benchmark datasets, while bagging, boosting, voting, and stacking ensembles yield further, consistent gains by exploiting complementary feature representations. Metaheuristic algorithms, including Particle Swarm, Genetic, Ant Colony, Grey Wolf, and Bayesian Optimization, are increasingly used to tune hyperparameters and ensemble weights, reducing computational overhead while preserving accuracy. Attention-augmented and hybrid CNN-transformer models, federated learning, and explainability tools such as Grad-CAM and SHAP are emerging as important extensions. Nevertheless, high computational cost, limited dataset diversity, restricted multimodal integration, susceptibility to adversarial perturbation, and weak interpretability continue to restrict clinical translation. The review concludes that optimized, explainable, and adversarially robust ensemble frameworks, validated through multi-center clinical trials, represent the most promising direction for translating deep learning research into dependable and deployable breast cancer detection systems.
References:-
References
World Health Organization, "Breast cancer," WHO Fact Sheets, 2022.
American Cancer Society, "Breast cancer early detection and diagnosis," 2024. [Online]. Available: https://www.cancer.org
N. Houssami and D. L. Miglioretti, "Digital breast tomosynthesis: A brave new world in breast cancer screening," Cancer, vol. 123, no. 4, pp. 611–618, 2017.
G. J. S. Litjens, T. Kooi, B. Ehteshami Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, and C. I. Sánchez, "A survey on deep learning in medical image analysis," Medical Image Analysis, vol. 42, pp. 60–88, 2017.
K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv:1409.1556, 2014.
D. A. Ragab, M. Sharkas, and O. Attallah, "Breast Cancer Diagnosis Using an Efficient CAD System Based on Multiple Classifiers," Diagnostics, vol. 9, no. 4, p. 165, 2019.
X. Zhao and X. Ma, "Robust ensemble learning for defending against adversarial attacks in medical imaging," IEEE Transactions on Medical Imaging, vol. 41, no. 5, pp. 1358–1370, 2022.
M. Kumar, U. Sriharsha, U. Prasanthi, S. Harika, and V. Devi, "Breast Cancer Detection using Deep Learning," 2024.
B. Hanife and B. Jale, "A novel medical image enhancement algorithm for breast cancer detection on mammography images using machine learning," Diagnostics, vol. 13, no. 3, p. 348, 2023.
M. Nasser and U. K. Yusof, "Deep learning based methods for breast cancer diagnosis: A systematic review and future direction," Diagnostics, vol. 13, no. 1, p. 161, 2023.
M. Alruily, M. Ezz, A. A. Mahmoud, H. Allahem, A. M. Mostafa, and H. Shabana, "Enhancing breast cancer detection in ultrasound images: An innovative approach using progressive fine-tuning of vision transformer models," International Journal of Intelligent Systems, vol. 2024, art. 6528752, 2024.
F. Alhsnony and L. Sellami, "Enhancing Breast Cancer Detection through Advanced Deep Learning: An Application of YOLOv8x on Mammographic Images," in Proc. 2024 IEEE 7th Int. Conf. Advanced Technologies, Signal and Image Processing (ATSIP), Sousse, Tunisia, 2024.
D. T. Meva, K. Popat, H. Kukadiya, and N. Jariwala, "Deep learning-based breast cancer detection with customized ensemble attention," Scientific Reports, vol. 16, art. 20022, 2026.
M. Dehghan Rouzi, B. Moshiri, M. Khoshnevisan, M. A. Akhaee, F. Jaryani, S. Salehi Nasab, and M. Lee, "Breast Cancer Detection with an Ensemble of Deep Learning Networks Using a Consensus-Adaptive Weighting Method," Journal of Imaging, vol. 9, no. 11, art. 247, 2023.
U. Sajid, R. A. Khan, S. M. Shah, and S. Arif, "Breast cancer classification using deep learned features boosted with handcrafted features," Biomedical Signal Processing and Control, vol. 86, art. 105353, 2023.
M. Sarathkumar, K. Dhanalakshmi, D. Madhivadhani, and V. Revathi, "A deep learning approach for efficient breast cancer diagnosis using hybrid CNN-BiLSTM with soft attention mechanism," 2024.
J. A. Sami and U. Arumugam, "An ensemble deep learning model for the detection and classification of breast cancer," Middle East Journal of Cancer, vol. 15, no. 1, pp. 40–51, 2024.
Y. Khourdifi, A. El Alami, M. Zaydi, Y. Maleh, and O. Er-Remyly, "Early breast cancer detection based on deep learning: An ensemble approach applied to mammograms," BioMedInformatics, vol. 4, no. 4, pp. 2338–2373, 2024.
S. R. Sannasi Chakravarthy, N. Bharanidharan, V. Vinoth Kumar, T. R. Mahesh, M. S. Alqahtani, and S. Guluwadi, "Deep transfer learning with fuzzy ensemble approach for the early detection of breast cancer," BMC Medical Imaging, vol. 24, no. 1, 2024.
D. Shah, M. A. U. Khan, M. Abrar, and M. Tahir, "Optimizing Breast Cancer Detection With an Ensemble Deep Learning Approach," International Journal of Intelligent Systems, vol. 2024, art. 5564649, 2024.
O. O. Oladimeji, I. McLoughlin, and S. Unnikrishnan, "Deep learning for breast cancer diagnosis: A bibliometric analysis and future research directions," 2024.
E. Hassan, A. Saber, S. El-Sappagh, and N. El-Rashidy, "Optimized ensemble deep learning approach for accurate breast cancer diagnosis using transfer learning and grey wolf optimization," Evolving Systems, vol. 16, art. 59, 2025.
R. O. Ogundokun, P. A. Owolawi, and C. Tu, "Optimized deep feature learning with hybrid ensemble soft voting for early breast cancer histopathological image classification," Computers, Materials & Continua, vol. 84, no. 3, pp. 4869–4885, 2025.
S. G. Finlayson, J. D. Bowers, J. Ito, J. L. Zittrain, A. L. Beam, and I. S. Kohane, "Adversarial attacks on medical machine learning," Science, vol. 363, no. 6433, pp. 1287–1289, 2019.
A. Amin, D. Acharya U, P. Koteshwara, S. P C, and S. Mathew, "A systematic literature review on mammography: Deep learning techniques for breast cancer detection with global and Asian perspectives," BMC Cancer, vol. 25, no. 1, art. 1627, 2025.