A novel unified Inception-U-Net hybrid gravitational optimization model (UIGO) incorporating automated medical image segmentation and feature selection for liver tumor detection

A novel unified Inception-U-Net hybrid gravitational optimization model (UIGO) incorporating automated medical image segmentation and feature selection for liver tumor detection

Abstract Segmenting liver tumors in medical imaging is pivotal for precise diagnosis, treatment, and evaluating therapy outcomes. Even with modern imaging technologies, fully automated segmentation systems have not overcome the challenge posed by the diversity in the shape, size, and texture of live...

Full description

Saved in:
Bibliographic Details
Main Authors: Tathagat Banerjee, Davinder Paul Singh, Pawandeep Kour, Debabrata Swain, Shubham Mahajan, Seifedine Kadry, Jungeun Kim
Format: Article
Language:English
Published: Nature Portfolio 2025-08-01
Series:Scientific Reports
Subjects:
Liver tumor segmentation
Medical imaging
UIGO
Deep learning
Image segmentation
Machine learning
Online Access:https://doi.org/10.1038/s41598-025-14333-0
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://doi.org/10.1038/s41598-025-14333-0

Similar Items