Year: 2025
Type: article
Abstract: Liver tumor segmentation is critical for accurate diagnosis and treatment planning. This system employs deep learning models such as ResNet-50 (main algorithm), DeepLabv3+ with ResNet-101, VGG-16, and Fully Convolutional Networks (FCNs) to segment liver and tumor regions from medical images. ResNet-50 is chosen for its efficiency and strong feature extraction. DeepLabv3+ captures multi-scale features, VGG-16 offers a strong baseline, and FCNs provide dense pixel-level predictions. These models improve segmentation accuracy and reduce reliance on manual annotation. Evaluation is performed using metrics like Dice Score and IoU. The approach aims to support radiologists with precise, automated tumor localization. This enhances early detection and improves clinical outcomes. **Index Terms—**ResNet-50, DeepLabv3+, FCN, VGG-16, Liver Tumor Segmentation, Medical Imaging.
DOI: 10.55041/ijsrem45425
Author: Karthik Kurupapu
Institution: GITAM University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4409644327