ArticleScientific reports2020
Comparison of handcrafted features and convolutional neural networks for liver MR image adequacy assessment.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
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- Deep-Stacked Convolutional Neural Networks for Brain Abnormality Classification Based on MRI Images.Journal of digital imaging · 2023Article
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- Deep learning in bioinformatics.Turkish journal of biology = Turk biyoloji dergisi · 2023Article
- A Novel Feature Selection Strategy Based on Salp Swarm Algorithm for Plant Disease Detection.Plant phenomics (Washington, D.C.) · 2023Article
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- SIFT-CNN: When Convolutional Neural Networks Meet Dense SIFT Descriptors for Image and Sequence Classification.Journal of imaging · 2022Article
- Wavelet Guided 3D Deep Model to improve Dental Microfracture Detection.Applications of medical artificial intelligence : first International Workshop, AMAI 2022, held in conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings. AMAI (Workshop) (1st : 2022 : Singapore ; Online) · 2022Article
- Article
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Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We propose a random forest classifier for identifying adequacy of liver MR images using handcrafted (HC) features and deep convolutional neural networks (CNNs), and analyze the relative role of these two components in relation to the training sample size. The HC features, specifically developed for this application, include Gaussian mixture models, Euler characteristic curves and texture analysis. Using HC features outperforms the CNN for smaller sample sizes and with increased interpretability. On the other hand, with enough training data, the combined classifier outperforms the models trained with HC features or CNN features alone. These results illustrate the added value of HC features with respect to CNNs, especially when insufficient data is available, as is often found in clinical studies.
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Registered trials
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