Transfer Learning Model for Breast Cancer Detection Using Mammograms from Low-Resource Settings

Abstract

Breast cancer remains a leading cause of cancer death that affects women worldwide, and the burden is felt most acutely in low resource settings where mammography access is scarce, and radiologist coverage is thin. Transfer learning-based deep learning models were evaluated for practical utility in breast cancer screening where imaging resources are restricted. The CBIS-DDSM dataset (Kaggle JPG version) was used for this study, with pathology labels mapped into a binary benign-versus-malignant classification scheme. The dataset consisted of 3,568 full mammograms and 3,461 ROI images from over 1,500 patients after quality control. 5-fold StratifiedGroupKFold cross-validation was used to prevent patient-level data leakage, which causes performance inflation in published studies, and ensured that no patient data combined training and validation datasets. The research evaluated two types of input data which included cropped region-of-interest lesion patches and complete mammogram images. InceptionV3 achieved the highest performance among ROI models by reaching ROC-AUC 0.821 and PR-AUC 0.775 and sensitivity 0.785. The study used full mammograms to evaluate ResNet50 performance which achieved ROC-AUC 0.838 and sensitivity 0.790 results. The study shows that transfer learning acts as a strong base for AI-assisted mammography triage systems in low-resource environments when patient level assessment continues throughout system development.