Exploring Breast Cancer CT Image Segmentation Techniques: A Comprehensive Survey

Breast cancer remains a significant global health concern, necessitating accurate and efficient diagnostic tools for early detection and treatment. This survey paper comprehensively reviews automated systems for breast cancer segmentation from CT scan images, aiming to provide a comprehensive overview of the current state-of-the-art techniques, challenges, and future directions in this domain. The survey encompasses a systematic analysis of methodologies employed in automated segmentation, including deep learning approaches, traditional machine learning algorithms, and hybrid models. Key findings reveal the advancements in image processing, feature extraction, and machine learning algorithms that have revolutionized breast cancer segmentation from CT scans. Moreover, the survey discusses the limitations and challenges faced by existing systems, such as data variability, interpretability, and generalization to diverse patient populations. The implications of automated breast cancer segmentation in clinical practice, including improved diagnostic accuracy, treatment planning, and patient outcomes, are highlighted. By synthesizing existing literature and identifying research gaps, this survey paper aims to guide future research efforts toward enhancing the effectiveness and reliability of automated systems for breast cancer segmentation from CT scan images.