Automated Breast Cancer Detection Using GLCM Texture Analysis and Ensemble Learning Techniques
Mammography is sometimes subject to intrinsic low contrast and noise, which results in a high degree of inter-observer variability among radiologists. This study investigates a classification system that focuses on textural feature analysis in the detection of malignancies at an early stage. Second-order statistical features, such as contrast, correlation, energy, and homogeneity, were used to detect the relationship between pixel intensities across space and reveal abnormal tissue growth using the Gray-Level Co-occurrence Matrix (GLCM). This study used 306 clinical mammograms (133 benign and 173 malignant) from the Curated Breast Imaging Subset of DDSM (CBIS-DDSM) to systematically benchmark five predictive models. Simpler models, such as Logistic Regression and SVM, demonstrated a low ability to determine a complex boundary in textual features, while XGBoost exhibited a higher discriminative ability. The XGBoost model achieved a maximum accuracy of 93.55% and a recall (sensitivity) rate of 94.29%. The findings demonstrate that integrating hand-crafted textural features and boosting techniques can mitigate false negative risks, offering a highly efficient and comprehensible instrument for clinical decision support in the screening of breast cancer.