ArticleJournal of digital imaging2014
Neural network ensemble based CAD system for focal liver lesions from B-mode ultrasound.
Article in Journal of digital imaging, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 89 citations in OpenAlex.
- Diagnostic Performance of Artificial Intelligence in Detection of Hepatocellular Carcinoma: A Meta-analysis.Journal of imaging informatics in medicine · 2024Pooled it
- Differentiation of focal liver lesions in contrast-enhanced ultrasound using a heuristic-guided hybrid machine-learning framework.Journal of medical ultrasonics (2001) · 2026Article
- Advancements in artificial intelligence applications for liver ultrasound imaging.BJR artificial intelligence · 2026Review
- Liver biopsy in the modern era: from traditional techniques to artificial intelligence and multi-omics integration.Frontiers in medicine · 2025Review
- Artificial intelligence in imaging for liver disease diagnosis.Frontiers in medicine · 2025Review
- Deep learning approach for discrimination of liver lesions using nine time-phase images of contrast-enhanced ultrasound.Journal of medical ultrasonics (2001) · 2024Article
- Artificial intelligence techniques in liver cancer.Frontiers in oncology · 2024Review
- Application of artificial intelligence in the diagnosis of hepatocellular carcinoma.eGastroenterology · 2023Review
- Radiological Diagnosis of Chronic Liver Disease and Hepatocellular Carcinoma: A Review.Journal of medical systems · 2023Review
- Artificial intelligence models for the diagnosis and management of liver diseases.Ultrasonography (Seoul, Korea) · 2023Article
- Artificial Intelligence in the Diagnosis of Hepatocellular Carcinoma: A Systematic Review.Journal of clinical medicine · 2022Review
- The feasibility to use artificial intelligence to aid detecting focal liver lesions in real-time ultrasound: a preliminary study based on videos.Scientific reports · 2022Article
- Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities.World journal of gastrointestinal oncology · 2022Review
- Artificial intelligence (AI) models for the ultrasonographic diagnosis of liver tumors and comparison of diagnostic accuracies between AI and human experts.Journal of gastroenterology · 2022Article
- Deep learning in breast imaging.BJR open · 2022Review
- Enhanced CT Textures Derived From Computer Mathematic Distribution Analysis Enables Arterial Enhancement Fraction Being an Imaging Biomarker Option of Hepatocellular Carcinoma.Frontiers in oncology · 2020Article
- An Automatic Parameter Decision System of Bilateral Filtering with GPU-Based Acceleration for Brain MR Images.Journal of digital imaging · 2019Article
- Liver Ultrasound Image Segmentation Using Region-Difference Filters.Journal of digital imaging · 2017Article
- IFCM Based Segmentation Method for Liver Ultrasound Images.Journal of medical systems · 2016Article
- Feature-Motivated Simplified Adaptive PCNN-Based Medical Image Fusion Algorithm in NSST Domain.Journal of digital imaging · 2016Article
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A neural network ensemble (NNE) based computer-aided diagnostic (CAD) system to assist radiologists in differential diagnosis between focal liver lesions (FLLs), including (1) typical and atypical cases of Cyst, hemangioma (HEM) and metastatic carcinoma (MET) lesions, (2) small and large hepatocellular carcinoma (HCC) lesions, along with (3) normal (NOR) liver tissue is proposed in the present work. Expert radiologists, visualize the textural characteristics of regions inside and outside the lesions to differentiate between different FLLs, accordingly texture features computed from inside lesion regions of interest (IROIs) and texture ratio features computed from IROIs and surrounding lesion regions of interests (SROIs) are taken as input. Principal component analysis (PCA) is used for reducing the dimensionality of the feature space before classifier design. The first step of classification module consists of a five class PCA-NN based primary classifier which yields probability outputs for five liver image classes. The second step of classification module consists of ten binary PCA-NN based secondary classifiers for NOR/Cyst, NOR/HEM, NOR/HCC, NOR/MET, Cyst/HEM, Cyst/HCC, Cyst/MET, HEM/HCC, HEM/MET and HCC/MET classes. The probability outputs of five class PCA-NN based primary classifier is used to determine the first two most probable classes for a test instance, based on which it is directed to the corresponding binary PCA-NN based secondary classifier for crisp classification between two classes. By including the second step of the classification module, classification accuracy increases from 88.7 % to 95 %. The promising results obtained by the proposed system indicate its usefulness to assist radiologists in differential diagnosis of FLLs.
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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.