Hybrid Approach Combining Ultrasound and Blood Test Analysis with A Voting Classifier for Accurate Liver Fibrosis and Cirrhosis Assessment
Year: 2025
Type: article
Abstract: Liver cirrhosis is an insidious condition involving the substitution of normal liver tissue with fibrous scar tissue and causing major health complications. The conventional method of diagnosis using liver biopsy is invasive and, therefore, inconvenient for use in regular screening. In this paper,we present a hybrid model that combines machine learning techniques with clinical data and ultrasound scans to improve liver fibrosis and cirrhosis detection accuracy is presented. The model integrates fixed blood test probabilities with deep learning model predictions (DenseNet-201) for ultrasonic images. The combined hybrid model achieved an accuracy of 92.5%. The findings establish the viability of the combined model in enhancing diagnosis accuracy and supporting early intervention in liver disease care.
Source: Journal of Neonatal Surgery
Authors: Sean Fargose, Kapil Kashyap, Chrisil Dabre, Fatema Dolaria, Nilesh Patil, Aniket Kore
Institution: Cooper Hospital
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4409951749