Hybrid Deep Learning Model for Breast Cancer Classification in Low-Middle Income Countries: A MobileNetV2 and Cubic SVM

Breast cancer is one of the major causes of female death in any part of the world, and the burden is highly disproportionately high in low-middle income countries (LMICs) because of the inaccessibility of diagnostic tools. The proposed paper suggests a lightweight hybrid deep learning model that integrates MobileNetV2 to extract features with a cubic Support Vector machine (SVM) to classify the features, specifically to be used in resource constrained settings. The model was trained and tested on the mini-DDSM dataset and obtained 78 percent overall accuracy and 70 percent recall on malignant cases and 98 percent recall on the normal cases. The computational analysis shows that the model is efficient and less than 20 ms per image inference on standard CPU hardware and a small footprint of 14 MB, which is reasonable to implement in regions with limited computational resources like LMICs. The hybrid architecture is especially strong in terms of false positive minimization (84 percent precision in all normal cases) and clinically relevant sensitivity in detecting malignancy. An interface built with Streamlit was created to showcase the practical use in low resource clinical environments.