Breast cancer is one of the most prevalent and life-threatening malignancies affecting women worldwide. According to the World Health Organization, breast cancer accounts for a substantial proportion of cancer-related mortality, with early detection and accurate diagnosis being critical determinants of patient survival and treatment efficacy.

While these traditional methods have contributed significantly to early detection, they are often limited by subjective interpretation, variability in radiologist expertise, and diagnostic errors, leading to both false positives and false negatives. These limitations underscore the urgent need for innovative approaches capable of enhancing diagnostic precision, reducing human error, and facilitating timely clinical decision-making.

 The integration of AI into breast cancer diagnostics is not merely a technological advancement; it represents a paradigm shift in clinical practice. AI-driven systems can assist radiologists in interpreting complex imaging data, prioritize high-risk cases, and reduce the cognitive burden associated with manual analysis. Furthermore, AI facilitates personalized medicine by enabling risk stratification, prognostic predictions, and treatment planning tailored to individual patient profiles. However, despite these promising outcomes, several challenges hinder the widespread adoption of AI in clinical settings. Key issues include the scarcity of large, annotated datasets, class imbalance in available data, algorithmic bias, and limited interpretability of deep learning models. The “black box” nature of DL algorithms often raises concerns among clinicians regarding transparency, accountability, and trust, which are critical for regulatory approval and patient safety. Additionally, the heterogeneity of imaging modalities, differences in acquisition protocols, and variations in population demographics necessitate robust generalization capabilities to ensure consistent performance across diverse clinical environments.

A systematic review of AI applications in breast cancer detection and diagnosis is essential to consolidate existing knowledge, identify prevailing trends, and highlight research gaps. Previous studies have predominantly focused on individual AI algorithms or specific imaging modalities, limiting the scope for comprehensive comparative analysis. Moreover, rapid advancements in computational power, algorithm design, and data availability over the past decade have significantly enhanced AI capabilities, warranting an updated synthesis of the literature. This review aims to bridge this gap by analyzing recent studies published between 2015 and 2025, encompassing both ML and DL approaches. Emphasis is placed on algorithmic performance, dataset characteristics, feature extraction techniques, evaluation metrics, and clinical applicability. By examining the strengths and limitations of various AI methodologies, this review seeks to provide a nuanced understanding of how AI can augment breast cancer diagnostics and inform future research directions.

From an analytical perspective, AI applications in breast cancer can be categorized into three primary domains: (1) imaging-based detection, (2) histopathological and genomic analysis, and (3) multi-modal predictive modeling. In imaging-based detection, CNNs have demonstrated superior capability in identifying subtle lesions in mammograms and ultrasound scans, often outperforming traditional ML models in sensitivity and specificity. Feature extraction in ML-based systems typically involves statistical, texture, or morphological descriptors, whereas DL models automatically derive discriminative features through hierarchical layers, capturing both low-level details and high-level semantic representations. Histopathological analysis has benefited from AI-driven segmentation and classification, enabling automated detection of malignant cells and tissue abnormalities with high precision. Multi-modal approaches, which integrate imaging, genomic, and clinical data, exemplify the next frontier in predictive oncology, allowing for more accurate risk assessment, prognosis, and treatment recommendation. Analytical comparisons across these domains reveal that while DL offers superior automation and performance, ML provides interpretability and robustness in smaller datasets, highlighting the complementary nature of these methodologies.

Despite these advances, several critical gaps remain in the current body of literature. First, most studies rely on retrospective datasets, which may not accurately reflect real-world clinical scenarios. Prospective validation and external testing across multiple institutions are essential to ensure generalizability. Second, the interpretability of AI models remains a pressing concern. Techniques such as saliency maps, Grad-CAM, and attention mechanisms provide partial insights, but further research is needed to make model decisions transparent and clinically actionable. Third, ethical and regulatory considerations, including data privacy, algorithmic fairness, and compliance with medical standards, must be addressed to facilitate integration into healthcare workflows. Finally, the cost-effectiveness and impact on clinical outcomes of AI-assisted diagnostics remain underexplored, necessitating comprehensive health economics studies. Table (1) shows the comparative analysis of previous studies on AI in breast cancer detection.

Table 1. Comparative Analysis of Previous Studies on AI in Breast Cancer Detection

Analysis and Comparison

1-       Machine Learning Vs Deep Learning:

ü  ML models (e.g., SVM, Random Forest) perform well with smaller datasets and are interpretable, which facilitates clinical acceptance.

ü  DL models (CNN, ResNet, DenseNet) achieve higher accuracy and sensitivity due to automated feature extraction, but they require large annotated datasets and are less interpretable.

2-       Explainable AI (XAI):

ü  XAI techniques, such as SHAP and Grad-CAM, increase clinician trust by showing how models reach decisions.

ü  While interpretability is improved, performance may slightly decrease compared to “black-box” DL models.

3-       Dataset Diversity:

ü  Multi-institutional datasets enhance generalizability and robustness.

ü  Low-resource settings present unique challenges such as small dataset size and variable image quality, which reduces model performance.

4-       Hybrid & Radiomics Approaches:

ü  Combining ML for feature selection with DL for classification improves performance and interpretability.

ü  Radiomics can extract subtle image features, enhancing early detection, especially for small lesions.

5-       Key Limitations Across Studies:

ü  Many studies face the “black-box” problem in DL models.

ü  Model generalization across different hospitals or populations is often limited.

ü  Computational cost and need for annotated datasets remain significant barriers.

 Materials and Methods

A systematic search was conducted in PubMed, IEEE Xplore, Scopus, and Web of Science for studies published between 2015–2025. Inclusion criteria were: (i) AI-based approaches for breast cancer detection or diagnosis, (ii) use of ML/DL algorithms, and (iii) performance evaluation using standard metrics such as accuracy, sensitivity, specificity, and AUC. Studies focusing on non-imaging data (e.g., genomics) were included to highlight multi-modal AI approaches.

Machine Learning Approaches

ML algorithms have been widely applied to breast cancer diagnosis using structured features extracted from medical images.

ü  Support Vector Machines (SVMs): Effective for high-dimensional data; commonly used for lesion classification.

ü  Random Forests (RF): Provide interpretability and handle feature heterogeneity.

ü  Ensemble methods: Combining multiple classifiers improves robustness and reduces bias.

Feature selection techniques such as PCA, LDA, and wavelet transforms enhance model performance by reducing dimensionality and highlighting discriminative characteristics. Reported accuracies typically range from 85–95%.

Table 2. Comparative Analysis of Previous Studies on Machine Learning Approaches in Breast Cancer Detection

Analysis and Comparison

1-       Techniques and Performance:

ü  SVM and Random Forest are the most commonly used ML techniques for breast cancer detection, showing consistent performance (Accuracy: 85–96%).

ü  Ensemble methods like XGBoost and Gradient Boosting improve robustness on heterogeneous datasets.

2-       Dataset Dependence:

ü  ML models perform well on structured or preprocessed datasets (e.g., Wisconsin Breast Cancer Dataset).

ü  Performance drops on raw imaging data (ultrasound, mammography) compared to deep learning approaches.

3-       Strengths of ML Approaches:

ü  High interpretability allows clinicians to understand decision-making.

ü  Lower computational resources than DL; suitable for smaller datasets or low-resource environments.

ü  Feature importance can guide clinical insights.

4-       Limitations of ML Approaches:

ü  Relies heavily on feature engineering; requires domain expertise.

ü  Less effective in handling high-dimensional imaging data without preprocessing.

ü  Generalization across hospitals or populations can be limited.

5-       Key Insights:

ü  ML approaches are ideal for small datasets and structured data.

ü  Hybrid models (ML + ANN or radiomics features) improve detection but increase complexity.

ü  While DL dominates in raw imaging tasks, ML remains relevant in interpretable AI, resource-constrained scenarios, and multi-modal integration.

Deep Learning Approaches

DL methods, particularly CNNs, automatically learn hierarchical features from raw images, reducing dependence on manual feature extraction.

ü  CNNs: Widely used for mammography, ultrasound, and histopathology image analysis.

ü  RNNs and LSTMs: Useful for sequential or temporal imaging data.

ü  Hybrid models: Combining ML and DL approaches for improved prediction.

DL models demonstrate high accuracy (often >95%), but challenges include large dataset requirements, overfitting, and interpretability concerns.

Table 3. Comparative Analysis of Previous Studies on Deep Learning Approaches in Breast Cancer Detection

Analysis and Comparison

1-       DL Techniques and Performance:

ü  CNNs and their variants (ResNet, VGG16, Dense Net, Efficient Net) dominate breast cancer detection tasks.

ü  Transfer learning and hybrid architectures improve accuracy, particularly for smaller datasets.

ü  Attention mechanisms enhance model focus on lesion regions, improving detection sensitivity.

2-       Dataset Characteristics:

ü  Large datasets (>7,000 images) are critical to achieve high performance in DL models.

ü  Multi-institutional datasets improve generalization but require significant computational resources.

3-       Strengths of DL Approaches:

ü  Automatic feature extraction from raw images reduces reliance on manual feature engineering.

ü  High accuracy and sensitivity compared to traditional ML, especially in complex imaging modalities (ultrasound, MRI).

ü  Hybrid and attention-based models enhance interpretability and clinical relevance.

4-       Limitations of DL Approaches:

ü  Black-box nature limits clinician trust without explainable AI (XAI) integration.

ü  Computationally intensive, requiring GPUs or high-performance computing for training.

ü  Model generalization may be limited if trained on single-center datasets.

5-       Key Insights:

ü  DL is superior to ML in raw image classification and multi-modal imaging tasks.

ü  Explainable DL approaches help balance performance and interpretability, which is crucial for clinical adoption.

ü  Transfer learning and hybrid DL-ML frameworks reduce data requirements while maintaining high accuracy.

Analytical Discussion

ü  Strengths: AI approaches reduce human error, accelerate diagnosis, and facilitate personalized treatment planning.

ü  Limitations: Lack of standardized datasets, class imbalance, and limited clinical validation hinder translation to practice.

ü  Trends: Multi-modal AI integrating imaging, genomics, and clinical data shows promise. Explainable AI is critical for clinician trust.

 Table 4. Analytical Comparison of Previous Studies on AI Approaches in Breast Cancer Detection

Analytical Discussion / Insights

1-       Performance Trends:

ü  DL models generally achieve higher accuracy and sensitivity compared to traditional ML due to automatic feature extraction and multi-layer representation of images.

ü  ML models maintain interpretability and lower computational costs, making them suitable for smaller datasets or low-resource environments.

2-       Hybrid Approaches:

ü  Combining ML with DL or integrating radiomics improves both accuracy and interpretability.

ü  Hybrid models balance automation with feature-level explain ability, addressing clinician trust issues.

3-       Explain ability:

ü  XAI approaches, including SHAP and attention mechanisms, improve transparency in DL models.

ü  Slight compromise in raw predictive performance is observed but is often offset by clinician trust and adoption.

4-       Dataset Implications:

ü  Large-scale, multi-institutional datasets enhance generalizability and robustness of DL models.

ü  Small or single-institution datasets limit model performance and external validation.

5-       Key Limitations Across Studies:

ü  DL models often suffer from the black-box problem.

ü  High computational costs and the need for GPU acceleration limit deployment in low-resource settings.

ü  Feature dependency in ML models and tuning complexity in hybrid models remain barriers to real-world adoption.

6-       Critical Takeaways:

ü  ML remains valuable where interpretability, small datasets, and low computational resources are priorities.

ü  DL dominates in raw image analysis and multi-modal imaging but requires strategies for explain ability, generalization, and efficiency.

ü  Future research should focus on multi-modal integration, explainable DL, and validation in diverse populations for clinical translation.

Discussion

The application of artificial intelligence (AI) in the early detection and diagnosis of breast cancer has emerged as a transformative area of research in medical imaging and oncology. This discussion synthesizes the findings from recent studies, comparing machine learning (ML) and deep learning (DL) approaches, highlighting their clinical relevance, and identifying the limitations and opportunities for future research.

Machine Learning Approaches: Machine learning techniques have long been employed in breast cancer diagnostics, relying on structured datasets derived from imaging, clinical records, and histopathological data. Algorithms such as support vector machines (SVMs), random forests (RF), k-nearest neighbors (KNN), and gradient boosting classifiers have demonstrated robust performance in distinguishing benign from malignant lesions. One key strength of ML approaches lies in their interpretability and flexibility, allowing clinicians to understand feature importance and decision-making processes. Feature engineering, including texture analysis, shape descriptors, and statistical measures, remains central to achieving high classification accuracy in ML-based systems.

However, the performance of ML models is highly dependent on the quality and quantity of annotated datasets. Smaller datasets and class imbalance can lead to overfitting or biased predictions, reducing generalizability across different populations or imaging modalities. To address these limitations, ensemble methods and cross-validation strategies are commonly employed. For instance, studies integrating multiple classifiers have demonstrated improved sensitivity and specificity, indicating that combining diverse models mitigates individual algorithmic weaknesses. Despite these advancements, ML approaches generally require manual feature extraction and domain expertise, which can limit scalability and automation in clinical workflows.

Deep Learning Approaches: Deep learning, particularly convolutional neural networks (CNNs), has revolutionized the field of breast cancer detection by enabling end-to-end analysis of raw imaging data without the need for handcrafted feature extraction. CNNs, recurrent neural networks (RNNs), and hybrid architectures automatically learn hierarchical representations from mammograms, ultrasound images, MRI scans, and histopathology slides. This capability has resulted in remarkable improvements in classification accuracy, sensitivity, and specificity, with several studies reporting performance exceeding 95%.

DL models are particularly effective in capturing complex spatial patterns, subtle tissue anomalies, and high-dimensional correlations that may be imperceptible to human observers. Moreover, advanced architectures, such as attention-based networks and generative adversarial networks (GANs), have enhanced model robustness, data augmentation, and interpretability. Multi-modal deep learning approaches, integrating imaging, genomic, and clinical data, have further demonstrated the potential to improve risk prediction and support personalized treatment strategies.

Despite these advantages, DL models face several challenges. Large volumes of labeled data are required for effective training, which may not be readily available due to privacy concerns or cost of annotation. Furthermore, the "black-box" nature of DL algorithms limits interpretability, making clinical adoption challenging. Explainable AI (XAI) techniques, such as saliency maps, Grad-CAM, and layer-wise relevance propagation, offer partial solutions but remain underdeveloped in terms of providing actionable clinical insights. Additionally, computational complexity and the need for high-performance hardware can hinder real-time deployment in resource-constrained healthcare settings.

Comparative Analysis of ML and DL: Analytically, ML and DL approaches are complementary rather than mutually exclusive. While ML provides interpretability, lower computational requirements, and effectiveness in smaller datasets, DL excels in automated feature extraction, scalability, and performance on large, high-dimensional datasets. Comparative studies indicate that hybrid systems—combining ML-based feature selection with DL classification—can harness the advantages of both paradigms, resulting in improved predictive accuracy and clinical applicability. Moreover, integration of ML and DL approaches allows for the development of semi-automated diagnostic pipelines that support radiologists in decision-making while retaining transparency and interpretability.

Clinical Relevance and Impact: The clinical integration of AI-based breast cancer diagnostics offers several potential benefits. Firstly, AI can significantly reduce diagnostic errors by providing consistent, objective, and reproducible analysis. Secondly, AI-assisted systems enhance workflow efficiency by prioritizing high-risk cases, thus optimizing radiologists’ time and attention. Thirdly, AI facilitates early detection, which is crucial for improving patient prognosis and enabling less invasive treatment interventions. Multi-modal AI systems that combine imaging, histopathology, and genomics allow for personalized treatment planning, risk stratification, and monitoring therapeutic responses, aligning with the principles of precision medicine.

However, translating AI into routine clinical practice requires rigorous validation, regulatory compliance, and trust-building among healthcare professionals. External validation on diverse, multi-institutional datasets is essential to ensure model generalizability and reliability. Furthermore, ethical considerations, including patient data privacy, algorithmic bias, and equity in access to AI-driven diagnostics, must be addressed to prevent unintended disparities in healthcare delivery.

Challenges and Limitations: Despite notable advances, several challenges limit the full-scale adoption of AI in breast cancer diagnostics. Data scarcity and heterogeneity remain prominent barriers, particularly for DL models requiring large, annotated datasets. Class imbalance, where malignant cases are underrepresented, can result in biased predictions and reduced sensitivity. Model interpretability remains a significant concern, as clinicians need to understand AI decision-making to trust and act upon its outputs. Additionally, differences in imaging protocols, equipment, and patient demographics across institutions complicate model generalization. Finally, regulatory hurdles, including approval by health authorities and compliance with medical device standards, pose practical challenges for AI deployment.

Future Directions: Future research should prioritize the development of multi-modal AI frameworks that integrate imaging, genomic, clinical, and lifestyle data to improve diagnostic accuracy and enable comprehensive risk assessment. Efforts to enhance model interpretability through explainable AI techniques are critical for clinician trust and regulatory approval. The creation of standardized, publicly available datasets and collaborative multi-institutional studies will facilitate robust model training and validation. Moreover, real-time, cloud-based AI diagnostic platforms have the potential to extend access to underserved regions and resource-constrained healthcare environments. Finally, longitudinal studies evaluating clinical outcomes, cost-effectiveness, and patient-centered benefits of AI-assisted diagnostics are necessary to establish its role in standard-of-care protocols.

A recent study has demonstrated that deep learning models, particularly Convolutional Neural Networks (CNNs), outperform traditional classification techniques in breast cancer detection. The research highlights the superior accuracy of CNNs in analyzing medical imaging data, emphasizing the importance of advanced neural network architectures in improving diagnostic outcomes. However, the study also acknowledges challenges such as the need for large annotated datasets and the complexity of model interpretability.

Table 5. Analytical Summary and Conclusion of AI Approaches in Breast Cancer Detection