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Published in 2021 at "European Radiology"
DOI: 10.1007/s00330-021-08198-w
Abstract: We aimed to develop and validate a deep convolutional neural network (DCNN) model for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) and its clinical outcomes using contrast-enhanced computed tomography (CECT) in a…
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Keywords:
preoperative prediction;
combined nomogram;
validation;
clinical outcomes ... See more keywords
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Published in 2018 at "Neural Processing Letters"
DOI: 10.1007/s11063-018-9878-5
Abstract: Abstract In recent years, deep learning especially deep convolutional neural networks (DCNN) has made great progress. Many researchers take advantage of different DCNN models to do object detection in remote sensing. Different DCNN models have different…
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Keywords:
object detection;
detection remote;
remote sensing;
different dcnn ... See more keywords
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Published in 2021 at "Measurement Science and Technology"
DOI: 10.1088/1361-6501/ac05f5
Abstract: The accurate identification of rolling bearing fault based on unbalanced data has always been a challenge in the field of fault diagnosis. In some practical scenarios, since the machine is in the normal state most…
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Keywords:
fault;
dense convolutional;
convolutional neural;
neural network ... See more keywords
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Published in 2017 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2017.2657778
Abstract: Deep convolutional neural networks (DCNNs) have recently emerged as a dominant paradigm for machine learning in a variety of domains. However, acquiring a suitably large data set for training DCNN is often a significant challenge.…
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Keywords:
imagery;
image;
remote sensing;
cover classification ... See more keywords
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Published in 2017 at "Journal of Applied Remote Sensing"
DOI: 10.1117/1.jrs.11.042614
Abstract: Abstract. We evaluated how deep convolutional neural networks (DCNN) could assist in the labor-intensive process of human visual searches for objects of interest in high-resolution imagery over large areas of the Earth’s surface. Various DCNN…
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Keywords:
search;
neural networks;
deep convolutional;
convolutional neural ... See more keywords
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Published in 2022 at "Computational Intelligence and Neuroscience"
DOI: 10.1155/2022/9082694
Abstract: To overcome the limitations of conventional breast screening methods based on digital mammography, a quasi-3D imaging technique, digital breast tomosynthesis (DBT) has been developed in the field of breast cancer screening in recent years. In…
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Keywords:
dcnn;
dbt images;
images using;
mass regions ... See more keywords
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Published in 2022 at "Journal of X-ray science and technology"
DOI: 10.3233/xst-211055
Abstract: BACKGROUND Processing Low-Intensity Medical Images (LI-MI) is difficult as outcomes are varied when it comes to manual examination, which is also a time-consuming process. OBJECTIVE To improve the quality of low-intensity images and identify the…
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Keywords:
dcnn;
classification;
leukemia classification;
deep learning ... See more keywords
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Published in 2020 at "Applied Sciences"
DOI: 10.3390/app10217898
Abstract: Current deep learning convolutional neural network (DCNN) -based hand gesture detectors with acute precision demand incredibly high-performance computing power. Although DCNN-based detectors are capable of accurate classification, the sheer computing power needed for this form…
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Keywords:
neural network;
spatial pyramid;
convolutional neural;
hand ... See more keywords
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Published in 2022 at "Diagnostics"
DOI: 10.3390/diagnostics12071564
Abstract: The aim of this study was to investigate the potential of a machine learning algorithm to classify breast cancer solely by the presence of soft tissue opacities in mammograms, independent of other morphological features, using…
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Keywords:
dcnn;
classification;
soft tissue;
tissue opacities ... See more keywords