scholarly journals Automated classification of osteomeatal complex inflammation on computed tomography using convolutional neural networks

2018 ◽  
Vol 9 (1) ◽  
pp. 46-52 ◽  
Author(s):  
Naweed I. Chowdhury ◽  
Timothy L. Smith ◽  
Rakesh K. Chandra ◽  
Justin H. Turner
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Automated Classification ◽  
Osteomeatal Complex

scholarly journals A comprehensive study on classification of COVID-19 on computed tomography with pretrained convolutional neural networks

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Tuan D. Pham
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
High Performance ◽  
Data Augmentation ◽  
Ct Scans ◽  
Automated Classification ◽  
Imaging Data ◽  
Use Of Data

Abstract The use of imaging data has been reported to be useful for rapid diagnosis of COVID-19. Although computed tomography (CT) scans show a variety of signs caused by the viral infection, given a large amount of images, these visual features are difficult and can take a long time to be recognized by radiologists. Artificial intelligence methods for automated classification of COVID-19 on CT scans have been found to be very promising. However, current investigation of pretrained convolutional neural networks (CNNs) for COVID-19 diagnosis using CT data is limited. This study presents an investigation on 16 pretrained CNNs for classification of COVID-19 using a large public database of CT scans collected from COVID-19 patients and non-COVID-19 subjects. The results show that, using only 6 epochs for training, the CNNs achieved very high performance on the classification task. Among the 16 CNNs, DenseNet-201, which is the deepest net, is the best in terms of accuracy, balance between sensitivity and specificity, $$F_1$$ F 1 score, and area under curve. Furthermore, the implementation of transfer learning with the direct input of whole image slices and without the use of data augmentation provided better classification rates than the use of data augmentation. Such a finding alleviates the task of data augmentation and manual extraction of regions of interest on CT images, which are adopted by current implementation of deep-learning models for COVID-19 classification.

scholarly journals Automated classification of bacterial cell sub-populations with convolutional neural networks

PLoS ONE ◽  
2020 ◽  
Vol 15 (10) ◽  
pp. e0241200
Author(s):  
Denis Tamiev ◽  
Paige E. Furman ◽  
Nigel F. Reuel
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Bacterial Cell ◽  
Automated Classification

scholarly journals Text Summarization and Classification of Clinical Discharge Summaries using Deep Learning

2020 ◽  
Author(s):  
Alizar Marchawala ◽  
Preetkumar Patel ◽  
Khushal Paresh Thaker ◽  
Hardik Gunjal ◽  
Abhishek nagrecha ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Diagnostic Procedure ◽  
Patient Discharge ◽  
Text Summarization ◽  
Automated Classification ◽  
Complex Features ◽  
Discharge Summaries

<p>This paper implements the automated classification of patient discharge notes into standard disease labels which includes the name of the diagnostic procedure required. In this approach, we use Convolutional Neural Networks to classify and represent complex features from the medical discharge summaries using the MT sample dataset. We make use of GloVE to have a pretrained model learn from it.<b></b></p>

scholarly journals Automated classification of three-dimensional reconstructions of coral reefs using convolutional neural networks

PLoS ONE ◽  
2020 ◽  
Vol 15 (3) ◽  
pp. e0230671 ◽  
Author(s):  
Brian M. Hopkinson ◽  
Andrew C. King ◽  
Daniel P. Owen ◽  
Matthew Johnson-Roberson ◽  
Matthew H. Long ◽  
...  
Keyword(s):  
Neural Networks ◽  
Coral Reefs ◽  
Convolutional Neural Networks ◽  
Three Dimensional ◽  
Automated Classification

scholarly journals Text Summarization and Classification of Clinical Discharge Summaries using Deep Learning

2020 ◽  
Author(s):  
Alizar Marchawala ◽  
Preetkumar Patel ◽  
Khushal Paresh Thaker ◽  
Hardik Gunjal ◽  
Abhishek nagrecha ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Diagnostic Procedure ◽  
Patient Discharge ◽  
Text Summarization ◽  
Automated Classification ◽  
Complex Features ◽  
Discharge Summaries

<p>This paper implements the automated classification of patient discharge notes into standard disease labels which includes the name of the diagnostic procedure required. In this approach, we use Convolutional Neural Networks to classify and represent complex features from the medical discharge summaries using the MT sample dataset. We make use of GloVE to have a pretrained model learn from it.<b></b></p>

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