Deep Neural Networks for Image Segmentation: A Review of Architectures, Training Methods, and Applications

Image segmentation is essential in computer vision, dividing images into meaningful segments for analysis. Recent deep learning advancements, using CNNs, FCNs, and U-Net, have greatly enhanced segmentation performance, benefiting medical imaging, autonomous vehicles, and satellite analysis. Challenges such as data scarcity, class imbalance, model generalization, and interpretability persist. This paper reviews advanced architectures and training methods, explores applications, and identifies current challenges and future research directions. It highlights the potential of semi-supervised and unsupervised learning, transfer learning, efficient models, and Explainable AI to address these issues, aiming to develop robust, efficient, and interpretable image segmentation models.

کلیدواژه ها:

Image Segmentation ، Deep Learning ، Convolutional Neural Networks ، Fully Convolutional Networks ، U-Net ، Medical Imaging