Deep Learning for Early Breast Cancer Detection and Personalized Treatment Strategies

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.

Deep Learning-Based Automated Skin Cancer Detection Using Dermoscopic Images

Deep Learning-Based Survival Prediction for Breast Cancer Using Whole Slide Histopathology Images

Breast cancer is currently one of the leading malignancies and mortality rates among women globally, creating an urgent need for accurate and efficient automated diagnostic tools. This study proposes and systematically compares convolutional neural network (CNN) architectures, including VG16, ResNet18, ResNet50, DenseNet121, EfficientNet-B0, and Swin-Transformer, applied to whole slide image (WSI) histopathology data for breast cancer prediction and classification. The models were trained and evaluated on the same WSI dataset with consistent image preprocessing techniques, allowing for objective comparison of performance. Experimental results showed that the performance of the models varied depending on the training strategy. In the non-fine-tune setting, ResNet18 achieved the best results with a C-index of 0.6587 and a Mean time-dependent AUC of 0.7317. When performing fine-tuning, ResNet50 outperformed the other architectures with a C-index of 0.6877 and a Mean AUC of 0.7738. Meanwhile, in the non-fine-tuned setup with combined clinical testing, Swin-Transformer achieved the highest performance with a C-index of 0.6793 and a Mean AUC of 0.7619.

Deep Learning-Based Segmentation, Classification, and Explainable Diagnosis of Breast Cancer Lesions

Breast cancer remains one of the most prevalent malignancies affecting women worldwide, and early, accurate detection through medical imaging is central to improving survival outcomes. This paper presents an end-to-end computer-aided diagnosis (CAD) framework that performs lesion segmentation, region-of-interest (ROI) extraction, tumor stage estimation, benign/malignant classification, and visual explainability across two complementary imaging modalities: digital mammography, represented by the Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM), and breast ultrasound, represented by the Breast Ultrasound Images (BUSI) dataset. For each modality, a modality-specific U-Net architecture was trained to generate binary lesion masks, from which tumor coverage percentage and a rule-based disease stage were derived. Three classifier backbones were benchmarked per modality — ResNet50, VGG16, and a custom convolutional neural network (CNN) for CBIS-DDSM (benign vs. malignant), and ResNet50, EfficientNet-B0, and a custom CNN for BUSI (benign vs. malignant vs. normal) — with Gradient-weighted Class Activation Mapping (Grad-CAM) applied to the strongest classifiers for interpretability. The trained pipelines were deployed in a Django-based web application allowing end users to upload an image and receive segmentation, ROI, stage, classification, confidence, and Grad-CAM outputs in real time. Results show a pronounced modality gap: the U-Net segmentation network achieved a validation Dice coefficient of 0.462 on BUSI versus 0.247 on CBIS-DDSM, and the best classifier reached 84.07% test accuracy on BUSI (ResNet50) compared with 61.86% on CBIS-DDSM (ResNet50), with markedly lower malignant-class recall on mammography (37%) than on ultrasound (76%). These findings quantify how imaging modality, lesion conspicuity, and dataset scale materially affect both segmentation and diagnostic performance, and they underscore the necessity of transparent, modality-aware reporting — including recall/sensitivity for the malignant class, not just aggregate accuracy — when evaluating deep learning systems intended to support breast cancer screening.

Breast Cancer Detection Using Transfer Learning Contribution

: Breast cancer remains one of the most critical health concerns for women, and early detection plays a major role in improving survival rates. With the rapid growth of deep learning techniques, computer-based systems have become increasingly effective at analyzing and classifying medical images. In this work, deep learning particularly transfer learning is used to identify breast cancer from histopathological images. Transfer learning allows us to take advantage of powerful models that have already been trained on vast image datasets and adapt them to our specific task. For this study, three well-known pre-trained convolutional neural network (CNN) architectures InceptionV3, EfficientNet, and ResNet50 were evaluated to determine which performs best for breast cancer classification. Each model was fine-tuned using our collection of breast tissue images. During experimentation, we noticed that some models had difficulty learning the necessary patterns for correct classification. Among them, ResNet50 stood out by producing more reliable and accurate predictions. The workflow followed several essential stages: images were preprocessed and resized according to each model’s input requirements, and data augmentation was applied to increase variety and prevent overfitt ing. After preparing the dataset, the models were trained and assessed using standard evaluation metrics, helping us compare their strengths and weaknesses. The results clearly indicate that the choice of architecture has a major impact on performance, esp ecially in medical image analysis. ResNet50’s deeper network and skip connections enabled it to capture fine-grained features within the images, leading to improved accuracy. This demonstrates the strong potential of deep learning techniques in healthcare. Beyond building an effective classifier, this project also emphasizes how artificial intelligence can support medical professionals in diagnosis. A dependable system can minimize human error, assist doctors in decision-making, and accelerate the detection process particularly in regions where experienced pathologists may be scarce. Overall, this work shows that transfer learning is a practical and efficient method for breast cancer detection and could contribute to better patient outcomes in the future.

An Explainable Deep Learning Framework for Early Skin Cancer Detection Using Dermoscopic Image Analysis

Experimental results indicate that the proposed framework achieves high diagnostic accuracy while providing interpretable insights that assist dermatologists in understanding model predictions.