ArticleJournal of biomedical physics & engineering2021
Implementation of Combinational Deep Learning Algorithm for Non-alcoholic Fatty Liver Classification in Ultrasound Images.
Article in Journal of biomedical physics & engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic accuracy of artificial intelligence models for imaging detection of hepatic steatosis through systematic review and meta analysis.Scientific reports · 2025Pooled it
- AI-powered ultrasound radiofrequency analysis for non-invasive pediatric liver fat quantification.Scientific reports · 2026Article
- Meta-Analysis of AI Integration in Abdominal Imaging for Liver Fibrosis and MASLD: Evaluating Diagnostic Accuracy and Clinical Impact.Journal of clinical medicine · 2025Review
- Diagnostic accuracy of convolutional neural networks in classifying hepatic steatosis from B-mode ultrasound images: a systematic review with meta-analysis and novel validation in a community setting in Telangana, India.The Lancet regional health. Southeast Asia · 2025Article
- Progress in the Application of Artificial Intelligence in Ultrasound-Assisted Medical Diagnosis.Bioengineering (Basel, Switzerland) · 2025Review
- Liver biopsy in the modern era: from traditional techniques to artificial intelligence and multi-omics integration.Frontiers in medicine · 2025Review
- Review
- Artificial intelligence in imaging for liver disease diagnosis.Frontiers in medicine · 2025Review
- Fatty liver classification via risk controlled neural networks trained on grouped ultrasound image data.Scientific reports · 2024Article
- Review
- Diagnosis of Salivary Gland Tumors Using Transfer Learning with Fine-Tuning and Gradual Unfreezing.Diagnostics (Basel, Switzerland) · 2023Article
- Nonalcoholic fatty liver disease (NAFLD) detection and deep learning in a Chinese community-based population.European radiology · 2023Article
- Radiological Diagnosis of Chronic Liver Disease and Hepatocellular Carcinoma: A Review.Journal of medical systems · 2023Review
- A fuzzy fine-tuned model for COVID-19 diagnosis.Computers in biology and medicine · 2023Article
- Artificial Intelligence for Detecting and Quantifying Fatty Liver in Ultrasound Images: A Systematic Review.Bioengineering (Basel, Switzerland) · 2022Review
- Ultrasound Radiomics for the Detection of Early-Stage Liver Fibrosis.Diagnostics (Basel, Switzerland) · 2022Article
- Artificial intelligence in liver ultrasound.World journal of gastroenterology · 2022Review
- Combined CNN and Pixel Feature Image for Fatty Liver Ultrasound Image Classification.Computational and mathematical methods in medicine · 2022Article
- Cascaded Deep Learning Neural Network for Automated Liver Steatosis Diagnosis Using Ultrasound Images.Sensors (Basel, Switzerland) · 2021Article
- Ultrasound Elastography-Cornerstone of Non-Invasive Metabolic Dysfunction-Associated Fatty Liver Disease Assessment.Medicina (Kaunas, Lithuania) · 2021Review
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundNowadays, fatty liver is one of the commonly occurred diseases for the liver which can be observed generally in obese patients. Final results from a variety of exams and imaging methods can help to identify and evaluate people affected by this condition.
objectiveThe aim of this study is to present a combined algorithm based on neural networks for the classification of ultrasound images from fatty liver affected patients. MATERIAL AND
methodsIn experimental research can be categorized as a diagnostic study which focuses on classification of the acquired ultrasonography images for 55 patients with fatty liver. We implemented pre-trained convolutional neural networks of Inception-ResNetv2, GoogleNet, AlexNet, and ResNet101 to extract features from the images and after combining these resulted features, we provided support vector machine (SVM) algorithm to classify the liver images. Then the results are compared with the ones in implementing the algorithms independently.
resultsThe area under the receiver operating characteristic curve (AUC) for the introduced combined network resulted in 0.9999, which is a better result compared to any of the other introduced algorithms. The resulted accuracy for the proposed network also caused 0.9864, which seems acceptable accuracy for clinical application.
conclusionThe proposed network can be used with high accuracy to classify ultrasound images of the liver to normal or fatty. The presented approach besides the high AUC in comparison with other methods have the independence of the method from the user or expert interference.
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