Hybrid Approach Combining Ultrasound and Blood Test Analysis with A Voting Classifier for Accurate Liver Fibrosis and Cirrhosis Assessment
DOI:
https://doi.org/10.63682/jns.v14i17S.4685Keywords:
Liver Cirrhosis, Liver Fibrosis, Ultrasound Imaging, Blood Test Analysis, Machine Learning, Deep Learning, Hybrid Model, Diagnostic Accuracy, Non-invasive Diagnosis, Clinical Data Integration, Predictive Modeling, Medical ImagingAbstract
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.
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https://www.kaggle.com/datasets/vibhingupta028/liver-histopathology-fibrosis-ultrasound-images/data
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