ArticleInternational journal of computer assisted radiology and surgery2021
Liver disease classification from ultrasound using multi-scale CNN.
Article in International journal of computer assisted radiology and surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 64 citations in OpenAlex.
- Applications of artificial intelligence (AI) in researches on non-alcoholic fatty liver disease(NAFLD) : A systematic review.Reviews in endocrine & metabolic disorders · 2022Pooled it
- A Review and Perspective of Techniques for Autonomous Robotic Ultrasound Acquisitions.Sensors (Basel, Switzerland) · 2026Review
- Multifeature Ultrasound-Based Classification for Breast Lesions: A Comparative Study of PONS Image Enhancement Technology.Mayo Clinic proceedings. Innovations, quality & outcomes · 2026Article
- Applications of Artificial Intelligence and Smart Devices in Metabolic Dysfunction-associated Steatotic Liver Disease.Journal of clinical and translational hepatology · 2026Article
- Article
- Advancements in artificial intelligence applications for liver ultrasound imaging.BJR artificial intelligence · 2026Review
- Explainable fusion of EfficientNetB0 and ResNet50 for liver fibrosis staging in ultrasound imaging.Scientific reports · 2025Article
- LivSCP: Improving Liver Fibrosis Classification Through Supervised Contrastive Pretraining.Diagnostics (Basel, Switzerland) · 2025Article
- 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
- FF Swin-Unet: a strategy for automated segmentation and severity scoring of NAFLD.BMC medical imaging · 2025Article
- Review
- Fatty liver classification via risk controlled neural networks trained on grouped ultrasound image data.Scientific reports · 2024Article
- Improving nonalcoholic fatty liver disease classification performance with latent diffusion models.Scientific reports · 2023Article
- Nonalcoholic fatty liver disease (NAFLD) detection and deep learning in a Chinese community-based population.European radiology · 2023Article
- The IMPACT framework and implementation for accessible in silico clinical phenotyping in the digital era.NPJ digital medicine · 2023Review
- Radiological Diagnosis of Chronic Liver Disease and Hepatocellular Carcinoma: A Review.Journal of medical systems · 2023Review
- Bio-Inspired Network for Diagnosing Liver Steatosis in Ultrasound Images.Bioengineering (Basel, Switzerland) · 2023Article
- Automated fatty liver disease detection in point-of-care ultrasound B-mode images.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- When liver disease diagnosis encounters deep learning: Analysis, challenges, and prospects.iLIVER · 2023Review
- Ultrasound image-based deep learning to differentiate tubal-ovarian abscess from ovarian endometriosis cyst.Frontiers in physiology · 2023Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeUltrasound (US) is the preferred modality for fatty liver disease diagnosis due to its noninvasive, real-time, and cost-effective imaging capabilities. However, traditional B-mode US is qualitative, and therefore, the assessment is very subjective. Computer-aided diagnostic tools can improve the specificity and sensitivity of US and help clinicians to perform uniform diagnoses.
methodsIn this work, we propose a novel deep learning model for nonalcoholic fatty liver disease classification from US data. We design a multi-feature guided multi-scale residual convolutional neural network (CNN) architecture to capture features of different receptive fields. B-mode US images are combined with their corresponding local phase filtered images and radial symmetry transformed images as multi-feature inputs for the network. Various fusion strategies are studied to improve prediction accuracy. We evaluate the designed network architectures on B-mode in vivo liver US images collected from 55 subjects. We also provide quantitative results by comparing our proposed multi-feature CNN architecture against traditional CNN designs and machine learning methods.
resultsQuantitative results show an average classification accuracy above 90% over tenfold cross-validation. Our proposed method achieves a 97.8% area under the ROC curve (AUC) for the patient-specific leave-one-out cross-validation (LOOCV) evaluation. Comprehensive validation results further demonstrate that our proposed approaches achieve significant improvements compared to training mono-feature CNN architectures ([Formula: see text]).
conclusionsFeature combination is valuable for the traditional classification methods, and the use of multi-scale CNN can improve liver classification accuracy. Based on the promising performance, the proposed method has the potential in practical applications to help radiologists diagnose nonalcoholic fatty liver disease.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.