Investigation of Diverse ML and DL Algorithms Toward the Segmentation and Classification of Mammographic Breast Density
Vishwayogita A. Savalkar, Gurpreet Singh Saini, Shivaji D. Pawar · 2025
Breast cancer is a deadly disease that usually affects women. Since a higher mammographic density is linked to a higher risk of developing breast cancer, it is crucial to consider this factor when determining an individual's risk for the illness. This makes it possible to guarantee thorough breast cancer screening and detection. With a focus on their possible applications in breast cancer prediction, this work investigates machine learning (ML) and deep learning (DL) methods for categorizing and segmenting mammography breast density. We demonstrate their applicability in therapeutic scenarios by comparing the performance of different algorithms. Its methods for categorizing and dividing mammographic breast density have shown to be incredibly effective. By offering accurate and consistent evaluations, these techniques can help radiologists make well-informed decisions. This survey summarizes the most recent state-of-the-art methods and covers developments in ML and DL algorithms for breast density segmentation and classification.