Independent evaluation of machine learning and deep learning models for breast cancer detection.

Breast cancer is one of the leading causes of cancer-related mortality worldwide, and despite advances in clinical diagnosis, challenges such as inconclusive imaging results and inter-observer variability highlight the need for complementary computational approaches to support early and accurate detection. In this study, we provide an overview of several machine learning (ML) and deep learning (DL) algorithms used for breast cancer detection with structured clinical data and imaging data. We applied ML models to structured clinical data which included Wisconsin Breast Cancer Dataset, CSAW-CC dataset and SEER database using Random Forest, Decision Tree, Logistic Regression, Gradient Boosting, and Bayesian classifier models. DL models, specifically Convolutional Neural Networks (CNN), ResNet50, VGG16, and DenseNet, were utilized in mammography, histopathology, and ultrasound datasets. The experimental results show that the DL models outperformed ML models in image classification tasks, obtaining accuracy rates of 97.88% for histopathological images and 92.00% for ultrasound data. However, ensemble-based and probabilistic ML models achieved strong performance on structured clinical data, with predictive accuracy of up to 98%. All performance metrics are reported with 95% confidence intervals.Model calibration was assessed using the Expected Calibration Error (ECE) and reliability diagrams. Logistic Regression, Gradient Boosting, and Random Forest produced the best-calibrated probability estimates among the structured-data classifiers, while the transfer-learning-based deep convolutional architectures were well calibrated on the histopathology imaging datasets. Calibration was comparatively weaker for Decision Tree and Naive Bayes among the structured-data classifiers, as well as for the from-scratch CNN baseline and some deep learning models evaluated on the smaller BUSI dataset. The results indicate that model performance is significantly influenced by data modality and that artificial intelligence may serve as a valuable computational tool to support clinical breast cancer diagnosis workflow. Further prospective validation using clinical data across diverse patient populations will be essential to confirm the robustness, interpretability, and generalizability of these findings.