Shahrood University of Technology Journal of AI and Data Mining 2322-5211 Articles in Press 2026 06 06 Enhanced Breast Cancer Detection using Hybrid Feature Extraction through Machine Learning and Deep Learning Techniques 3803 10.22044/jadm.2026.17095.2846 EN Naga Subrahmanyeswari Nimmakayala Jawaharlal Nehru Technological University Krishna Prasad M H M Jawaharlal Nehru Technological University Kakinada Journal Article 2025 11 10 Breast cancer detection is critical for early diagnosis and treatment. This paper utilized the BreakHis dataset, comprising 7,907 histopathological images of breast tumors (benign and malignant) captured at varying magnification levels. Initially, a basic CNN was applied, followed by advanced deep learning architectures including ResNet, EfficientNet, Mobilenet, Densenet and VGG19. Among these models, ResNet achieved the highest accuracy of 90.2%. For improving performance, a hybrid combination of hand-crafted features (pHash, HOG, GLCM, Hu Moments, SIFT, ORB and LBP) and transfer learning features (EfficientNet, DenseNet, ResNet, VGG19 MobileNet) was considered. Combined, these features were merged into a single feature vector, and were classified using ML algorithms: Logistic Regression, Naive Bayes, KNN, Decision Tree, Random Forest, Gradient Boosting and XGBoost. XGBoost yielded the highest accuracy of 96.2%. Additionally, deep learning models including Multilayer Perceptron (MLP) and Artificial Neural Networks (ANN) were explored, with ANN slightly outperforming MLP, achieving an accuracy of 98.3% compared to 97.5% for MLP. The results highlight the efficacy of combining traditional and deep learning-based features for improved diagnostic accuracy