Innovative Machine Learning Framework for Mammographic Breast Cancer Detection

Abstract

Breast cancer remains a major global health challenge, with early and accurate detection being critical for improving patient outcomes. However, traditional mammogram-based diagnostic approaches often face limitations such as high noise levels, low feature resolution, and suboptimal classification accuracy. This study addresses these gaps by introducing an innovative framework that integrates advanced preprocessing, feature extraction, and machine learning classification techniques. The preprocessing phase employs the Mean Error Splash Filter, specifically designed for mammographic images, to reduce noise while preserving diagnostic features. To bridge the gap in adaptive feature selection, the Tech Bee algorithm extracts critical features such as texture, edges, and region properties, prioritizing those with high diagnostic relevance. Using a stratified dataset, a Gradient Vector Boosting Classifier is applied for robust classification, capable of handling nonlinear relationships and imbalanced datasets. The proposed methodology achieved high accuracy, sensitivity, and specificity, outperforming traditional methods and offering a significant advancement in breast cancer prediction. By addressing current challenges in noise reduction, feature extraction, and classification, this study provides a scalable and efficient tool for early breast cancer detection and paves the way for improved diagnostic interventions.