Deep Learning and Ensemble Learning Techniques for Breast Cancer Detection: A Review of Recent Advances
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.