Quantifying Breast Cancer: Radiomics, Machine Learning, and Dimensionality Reduction for Enhanced Image-Based Diagnosis

Abstract

Radiomics allows for measuring tumor heterogeneity, discovering prognostic biomarkers, early detection and diagnosis, and combining with machine learning to improve clinical decision-making. Radiomics is essential for obtaining quantitative characteristics from medical pictures, such as those acquired from radiological scans such as MRI, CT, or PET scans. The characteristics include many qualities such as shape, texture, intensity, and spatial relationships within the images. Radiomics is crucial for extracting features by turning medical images into quantitative data that capture detailed aspects of tissue architecture and physiology. The identified traits could significantly transform clinical decision-making in oncology and other areas. This study aims to enhance existing breast cancer diagnostic techniques by utilizing radiomics to detect the disease at an early stage. Our study intends to enhance diagnostic accuracy by utilizing machine learning models and dimensionality reduction approaches on radiomics characteristics. We provide a new technique that integrates dimensionality reduction with machine learning algorithms to examine radiomics characteristics collected from breast cancer images, improving early breast cancer detection. The proposed method is comprehensively evaluated, showing significant enhancements in diagnostic accuracy for early-stage breast cancer when compared to conventional methods. The proposed model has an accuracy of 88.72% as compared to recent works as mentioned in Table 3. The results suggest that radiomics-based techniques could enhance breast cancer screening by identifying subtle imaging indicators.