Mass Detection In Mammograms Using Computer Vision And Machine Learning
Sindhu Muralidhara Havaldar · 2023
This study explores mammogram mass segmentation, focusing on computer vision and machine learning methods while avoiding neural networks.Despite their exceptional performance, neural networks demand substantial resources and data, presenting challenges.Our research aims to uncover the potential of non-neural models for effective mass segmentation in mammograms.This approach is not resource intensive and can be used for real-time mass segmentation in CAD systems.The steps involved in this process include: eliminating background noise through binarization through otsu-thresholding, enhancing images using a unique histogram equalization method,segmenting mammograms with Gaussian Mixture models to isolate crucial patterns, extracting potential abnormalities and filtering falsepositives using classification algorithms.Notably, we prioritize sensitivity over specificity, tailored to our application's needs.This thorough exploration into non-neural models intends to provide insights into their effectiveness for mammogram mass segmentation, offering potential resource-efficient alternatives in situations where neural networks are impractical.v