MEDICAL IMAGE SEGMENTATION USING DEEP LEARNING MODELS
Keywords:
Medical image segmentation, Deep Learning, U-Net architecture, CNN (Convolutional Neural Networks), Transformer models, GAN (Generative Adversarial Networks), Diffusion models, MRI (Magnetic Resonance Imaging), CT (Computed Tomography), Ultrasound images, Histopathological images, Explainable AI (XAI), Federated Learning, Multimodal integration, Clinical decision support systems.Abstract
Medical image segmentation is one of the most critical areas in clinical diagnostics and treatment planning today. Segmentation technologies serve as reliable tools for physicians in early disease detection, optimizing treatment strategies, and patient monitoring. Due to the limitations of classical algorithms and their low effectiveness on noisy images, Deep Learning architectures — U-Net, CNN, Transformer, GAN, and Diffusion models — have recently ushered in a new era in segmentation.
This article not only provides a theoretical and technical analysis of these models but also highlights their clinical applications in MRI, CT, ultrasound, and histopathological images. Furthermore, it examines the importance of innovative approaches such as Explainable AI, Federated Learning, and AR/VR integration in clinical practice.
