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 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 Automatic Evaluation of the Lung Condition of COVID-19 Patients Using X-ray Images and Convolutional Neural Networks

2021 ◽  
Vol 11 (1) ◽  
pp. 28
Author(s):  
Ivan Lorencin ◽  
Sandi Baressi Šegota ◽  
Nikola Anđelić ◽  
Anđela Blagojević ◽  
Tijana Šušteršić ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Clinical Picture ◽  
Data Augmentation ◽  
Lower Amount ◽  
Training Procedure ◽  
X Ray ◽  
Severe Clinical Picture ◽  
Lung Condition

COVID-19 represents one of the greatest challenges in modern history. Its impact is most noticeable in the health care system, mostly due to the accelerated and increased influx of patients with a more severe clinical picture. These facts are increasing the pressure on health systems. For this reason, the aim is to automate the process of diagnosis and treatment. The research presented in this article conducted an examination of the possibility of classifying the clinical picture of a patient using X-ray images and convolutional neural networks. The research was conducted on the dataset of 185 images that consists of four classes. Due to a lower amount of images, a data augmentation procedure was performed. In order to define the CNN architecture with highest classification performances, multiple CNNs were designed. Results show that the best classification performances can be achieved if ResNet152 is used. This CNN has achieved AUCmacro¯ and AUCmicro¯ up to 0.94, suggesting the possibility of applying CNN to the classification of the clinical picture of COVID-19 patients using an X-ray image of the lungs. When higher layers are frozen during the training procedure, higher AUCmacro¯ and AUCmicro¯ values are achieved. If ResNet152 is utilized, AUCmacro¯ and AUCmicro¯ values up to 0.96 are achieved if all layers except the last 12 are frozen during the training procedure.

USE OF CONVOLUTIONAL NEURAL NETWORKS FOR X-RAY IMAGE ORIENTATION DETERMINATION

2021 ◽  
Author(s):  
Sandi Baressi Šegota ◽  
◽  
Simon Lysdahlgaard ◽  
Søren Hess ◽  
Ronald Antulov
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
High Performance ◽  
Data Augmentation ◽  
Classification Model ◽  
Orientation Sensitivity ◽  
X Ray ◽  
Artificial Neural Network Ann ◽  
Image Orientation ◽  
Single Orientation

The fact that Artificial Intelligence (AI) based algorithms exhibit a high performance on image classification tasks has been shown many times. Still, certain issues exist with the application of machine learning (ML) artificial neural network (ANN) algorithms. The best known is the need for a large amount of statistically varied data, which can be addressed with expanded collection or data augmentation. Other issues are also present. Convolutional neural networks (CNNs) show extremely high performance on image-shaped data. Despite their performance, CNNs exhibit a large issue which is the sensitivity to image orientation. Previous research shows that varying the orientation of images may greatly lower the performance of the trained CNN. This is especially problematic in certain applications, such as X-ray radiography, an example of which is presented here. Previous research shows that the performance of CNNs is higher when used on images in a single orientation (left or right), as opposed to the combination of both. This means that the data needs to be differentiated before it enters the classification model. In this paper, the CNN-based model for differentiation between left and right-oriented images is presented. Multiple CNNs are trained and tested, with the highest performing being the VGG16 architecture which achieved an Accuracy of 0.99 (+/- 0.01), and an AUC of 0.98 (+/- 0.01). These results show that CNNs can be used to address the issue of orientation sensitivity by splitting the data in advance of being used in classification models.

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 A Comprehensive Study on Classification of COVID-19 on Computed Tomography with Pretrained Convolutional Neural Networks

2020 ◽  
Author(s):  
Tuan Pham
Keyword(s):  
High Performance ◽  
Data Augmentation ◽  
Use Of Data ◽  
Current Implementation ◽  
Direct Input ◽  
Rapid Deployment ◽  
Very High ◽  
Manual Extraction ◽  
Comprehensive Study

<div>This study presents an investigation on sixteen 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 update CNNs that achieve very high performance on the classification task and discover that implementation of transfer learning with direct input of whole image slices and without the use of data augmentation provide better classification results than the use of data augmentation. </div><div><br></div><div>{\it Conclusions:} The findings alleviate 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, and can facilitate the rapid deployment of AI tools to contain the spread of the coronavirus disease. </div><div><br></div>

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