2026-10-12T13:46:21Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34016561 2026-09-28T17:35:25Z category_146 category_734 category_931 category_811 category_64 portal_5 item_type_3 month_year_09_2026

<p>Summary of the datasets used in this study.</p> Deepthi (25132691) Ashalatha Nayak (11818868) Rani Oomman Panicker (25132694) Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified Mathematical Sciences not elsewhere classified Cancer valuable computational tool scratch cnn baseline related mortality worldwide random forest produced observer variability highlight naive bayes among image classification tasks complementary computational approaches cnn ), resnet50 bayesian classifier models experimental results show smaller busi dataset obtaining accuracy rates inconclusive imaging results expected calibration error applied ml models several machine learning breast cancer detection deep learning models structured clinical data histopathology imaging datasets imaging data clinical diagnosis machine learning deep learning results indicate ultrasound datasets predictive accuracy cc dataset accurate detection ultrasound data data modality data classifiers dl models support early significantly influenced reliability diagrams performance metrics model performance model calibration logistic regression leading causes independent evaluation histopathological images gradient boosting <div><p>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.</p></div> 2026-09-28T17:35:15Z Dataset Dataset 10.1371/journal.pdig.0001747.t001 https://figshare.com/articles/dataset/_p_Summary_of_the_datasets_used_in_this_study_p_/34016561 CC BY 4.0