Advanced Breast Cancer Diagnostics through a Comparative Analysis of SVM, Random Forests, and Neural Networks in MRI Image Analysis

Breast cancer, a predominant health concern globally, necessitates advanced diagnostic tools for timely and precise detection. This study endeavored to amalgamate the capabilities of magnetic resonance imaging (MRI) scans with machine learning (ML) to foster enhanced diagnostic accuracy. Employing a comprehensive dataset sourced from three major hospitals, our approach utilized preprocessing techniques to refine MRI image quality, followed by intricate feature extraction focusing on shape, texture, and intensity. Three ML models were implemented, with the Random Forests model emerging as the standout, achieving an impressive accuracy of 92%. This represents a notable improvement over traditional MRI analysis, which registered an accuracy of 84%. When benchmarked against contemporary methods like Deep Learning ConvNets at 88% and Gradient Boosted Trees at 87%, our method consistently outperformed. The results underscore the potential of integrating advanced computational models with medical imaging, promising more reliable and early breast cancer detection. This research serves as a testament to the profound impact of technology on medical diagnostics, offering a promising direction for future endeavors in the realm of breast cancer detection.