Advancements in deep learning for early breast cancer detection using artificial intelligence models in low resource settings

Abstract

Breast cancer remains a leading cause of mortality among women in low- and middle-income countries (LMICs), compounded by fewer radiologists available and resources for diagnosis. A narrative review that summarizes 45 peer-reviewed publications to date from 2018 to 2025 is presented for deep learning (DL) models for mammography detection for breast cancer, targeting low-resource-critical architectures for development in LMIC settings. We compare convolutional neural networks (CNNs), hybrid CNN-support vector machine (SVM) models, recurrent/LSTM networks, and lightweight architectures such as MobileNet and EfficientNet. In quantitative synthesis the reported diagnostic accuracy for MobileNet is between 89 and 92%, supported with 128 MB RAM and 0.3 GFLOPs. Therefore it is appropriate for offline edge devices (smartphones, NVIDIA Jetson Nano). EfficientNet achieves 91–94% accuracy—though it requires stable internet to enable cloud deployment, due to its greater parameter size (5.3 million). Both architectures reduce false positives by 7 to 69% and false negatives by 9% compared to traditional computer-aided detection, though most evidence is retrospective. The most important contributions from this review are: (1) a quantitative comparison of efficiency for edge versus cloud deployment, (2) detection of dataset bias (no LMIC-specific validation exists), and (3) practical recommendations regarding infrastructure, regulatory pathways, and privacy-preserving federated learning. Limitations include cross-study heterogeneity in datasets (CBIS-DDSM, INbreast, MIAS) and evaluation protocols. We conclude that MobileNet and EfficientNet provide good trade-offs for LMICs but call for prospective trials and locally curated datasets before clinical implementation.