Development and Analysis of an AI-Driven System for Automated Liver Segmentation in Medical Imaging using Deep Learning

Abstract

Liver segmentation is crucial for medical imaging in order to detect and treat liver-related disorders. Deep learning algorithms have been used to increase the automation and precision of segmentation. CNNs, such as transformer-based models, generative adversarial networks (GANs), and U-Net variants, have demonstrated notable performance improvements among these. Nonetheless, challenges such as interpretability, ethical constraints, and clinical application remain significant. Explainable AI (XAI) has arisen as a groundbreaking resource that provides clarity into model decision-making processes, thus improving clinician confidence and ensuring safe use in medical settings. XAI connects the divide between cutting-edge research and practical clinical application by tackling challenges like training data biases, patient confidentiality, and responsibility. By deployment of interpretable models, AI-assisted liver segmentation becomes ethical and trustworthy while allowing for real-time segmentation and personalized treatment