A Robust Approach for Image Segmentation using U-Net Model
DOI:
https://doi.org/10.15662/IJEETR.2026.0802177Keywords:
U-Net, image segmentation, deep learning, medical imaging, convolutional neural networks, semantic segmentation, computer visionAbstract
Image segmentation is a fundamental task in computer vision that involves partitioning an image into meaningful regions for analysis. This paper presents a deep learning-based approach for image segmentation using the U-Net architecture. The model employs an encoder-decoder structure with skip connections to effectively capture both spatial and contextual information. The input images are preprocessed and fed into the network, which learns to generate accurate segmentation masks. Experimental results demonstrate that the proposed U-Net model achieves improved segmentation performance with higher accuracy and reduced loss. The predicted outputs closely match the ground truth masks, indicating the effectiveness of the model in identifying object boundaries. The proposed approach can be applied to various applications such as medical imaging, object detection, and scene understanding.
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