Citation

Alazaidah, Raed and Samara, Ghassan and Abu Asi, Hamza and Abuowaida, Suhaila and Mashagba, Hamza A. and Abd Aziz, Azlan and Larguech, Samia and Al-Bawri, Samir S. (2025) Breast Cancer Classification Using Deep Feature Extraction and Machine Learning. HighTech and Innovation Journal, 6 (4). pp. 1282-1299. ISSN 2723-9535

Text 9 (3).pdf - Published Version Restricted to Repository staff only Download (1MB)

Abstract

Early and accurate breast cancer diagnosis remains critical yet challenging in routine practice. This study proposes a simple, reproducible pipeline that combines deep feature extraction from pre-trained CNNs (ResNet50, VGG16, EfficientNet-B0, DenseNet121, MobileNetV2) with classical machine-learning classifiers (logistic regression, SVM, k-NN, decision tree, random forest, gradient boosting, XGBoost, LightGBM, Naïve Bayes, and MLP). Features are computed after standardized preprocessing; class imbalance is addressed with SMOTE when present. We evaluate three image datasets (binary and multiclass) using accuracy, precision, recall/sensitivity, F1, and confusion matrices, and apply paired statistical tests across cross-validation splits. Findings: EfficientNet-B0+MLP and ResNet50+MLP achieve peak accuracies up to 99.6% on highquality, balanced data, while DenseNet121+MLP with SMOTE attains 97.8% on imbalanced multiclass data. SMOTE yields substantial gains on imbalanced data and negligible effect on balanced sets; decision trees underperform consistently. Novelty/Improvement: Rather than a monolithic end-to-end network, we provide a modular, resource-aware blueprint that (i) disentangles feature extraction from classification, (ii) quantifies when imbalance correction matters, and (iii) reports clinically relevant error types. We further outline explainability with Grad-CAM/SHAP and discuss inference-time tradeoffs and real-world workflow integration, offering an interpretable and deployment-friendly alternative to heavier end-toend models.

Item Type:	Article
Uncontrolled Keywords:	Machine learning, image processing
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) > TA1501-1820 Applied optics. Photonics
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	06 Feb 2026 08:28
Last Modified:	06 Feb 2026 08:28
URII:	http://shdl.mmu.edu.my/id/eprint/15211

Downloads

Edit (login required)