International Journal on Science and Technology

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Non-Alcoholic Hepatic Steatosis Grading: Exploring the AI Potential of Machine vs Deep Learning

Author(s) NEELAM CHITTORA, ANURAG CHITTORA, ANUSHKA CHITTORA, ANSH GARG
Country India
Abstract Abstract—Non-alcoholic fatty liver disease(NAFLD)is a grow- ing global health concern and a leading cause of chronic liver disease. Accurate, early-stage detection and grading of hepatic steatosis are essential to prevent progression to more severe con- ditions such as cirrhosis or hepatocellular carcinoma. This study evaluates the performance of classical machine learning and deep learning approaches for automated classification of liver steatosis using ultrasound (US) images from the BEHSOF dataset, which includes annotated clinical metadata. Preprocessing techniques such as grayscale normalization and Local Binary Pattern (LBP) feature extraction were employed to enhance diagnostic features. Three models—Support Vector Machine(SVM), Random Forest (RF), and an Artificial Neural Network (ANN)—were developed and tested. The ANN achieved the highest accuracy (99.09%), outperforming both RF(99.0%)and SVM(80%)classifiers, and surpassing previously reported Inception-ResNet-v2 results. These findings highlight the potential of interpretable and computationally efficient AI systems in supporting ultrasound- based NAFLD assessment, especially
Keywords Key Words—Fatty Liver, Deep Learning, Ultrasound Imag- ing, Machine Learning, Convolutional Neural Networks (CNN), Transfer Learning, Feature Extraction, Medical Image Classifi- cation, Non-Alcoholic Fatty Liver Disease (NAFLD), Computer- Aided Diagnosis (CAD)
Field Engineering
Published In Volume 16, Issue 3, July-September 2025
Published On 2025-07-30
DOI https://doi.org/10.71097/IJSAT.v16.i3.7433
Short DOI https://doi.org/g9vzdz
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