scholarly journals Tuberculosis Detection from Computed Tomography with Convolutional Neural Networks

2019 ◽  
Vol 08 (04) ◽  
pp. 47-56
Author(s):  
Xudong Liu ◽  
Haoxiang Lei ◽  
Sicun Han
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Convolutional Neural Networks

scholarly journals External validation and transfer learning of convolutional neural networks for computed tomography dental artifact classification

2020 ◽  
Vol 65 (3) ◽  
pp. 035017 ◽  
Author(s):  
Mattea L Welch ◽  
Chris McIntosh ◽  
Alberto Traverso ◽  
Leonard Wee ◽  
Tom G Purdie ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
External Validation

An artificial intelligence algorithm that differentiates anterior ethmoidal artery location on sinus computed tomography scans

2019 ◽  
Vol 134 (1) ◽  
pp. 52-55 ◽  
Author(s):  
J Huang ◽  
A-R Habib ◽  
D Mendis ◽  
J Chong ◽  
M Smith ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Computed Tomography ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Sinus Surgery ◽  
Computed Tomography Scans ◽  
Ethmoidal Artery ◽  
Anterior Ethmoidal Artery

AbstractObjectiveDeep learning using convolutional neural networks represents a form of artificial intelligence where computers recognise patterns and make predictions based upon provided datasets. This study aimed to determine if a convolutional neural network could be trained to differentiate the location of the anterior ethmoidal artery as either adhered to the skull base or within a bone ‘mesentery’ on sinus computed tomography scans.MethodsCoronal sinus computed tomography scans were reviewed by two otolaryngology residents for anterior ethmoidal artery location and used as data for the Google Inception-V3 convolutional neural network base. The classification layer of Inception-V3 was retrained in Python (programming language software) using a transfer learning method to interpret the computed tomography images.ResultsA total of 675 images from 388 patients were used to train the convolutional neural network. A further 197 unique images were used to test the algorithm; this yielded a total accuracy of 82.7 per cent (95 per cent confidence interval = 77.7–87.8), kappa statistic of 0.62 and area under the curve of 0.86.ConclusionConvolutional neural networks demonstrate promise in identifying clinically important structures in functional endoscopic sinus surgery, such as anterior ethmoidal artery location on pre-operative sinus computed tomography.

Effective Property Uncertainty of Graphite Electrodes from Computed Tomography Using Bayesian Convolutional Neural Networks

2020 ◽  
Vol MA2020-01 (2) ◽  
pp. 448-448
Author(s):  
Chance Norris ◽  
Tyler LaBonte ◽  
Carianne Martinez ◽  
Scott A. Roberts ◽  
Partha P. Mukherjee
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Effective Property ◽  
Graphite Electrodes

scholarly journals Automatic Segmentation of Stroke Lesions in Non-Contrast Computed Tomography Datasets With Convolutional Neural Networks

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 94871-94879 ◽  
Author(s):  
Anup Tuladhar ◽  
Serena Schimert ◽  
Deepthi Rajashekar ◽  
Helge C. Kniep ◽  
Jens Fiehler ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Automatic Segmentation

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.

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