Advanced feature extraction for mammogram mass classification: a multi-scale multi-orientation framework

Abstract Breast cancer remains one of the most prevalent and life-threatening diseases affecting women worldwide. Early detection is crucial for improving survival rates and treatment outcomes. This study proposes an advanced feature extraction method for classifying mammogram masses by combining multi-scale multi-orientation (MSMO) Gabor wavelets and gray-level co-occurrence matrix (GLCM) statistical features. MSMO Gabor filters extract detailed texture information across multiple scales and orientations, while GLCM captures statistical spatial relationships between pixel intensities. A feature selection process refines these features, enhancing classification accuracy. Experiments using Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM) datasets validate the approach with machine learning classifiers, including random forest (RF), decision tree (DT), support vector machine (SVM), k-nearest neighbors (k-NN), and deep neural network (DNN). RF outperformed other models and achieved 96.64% accuracy on MIAS dataset and 95.90% on DDSM dataset. Our approach shows the efficacy of optimally combining MSMO Gabor and GLCM features to advance computer-aided diagnosis systems for early and precise breast cancer detection.