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Effect of Pre-processing of CT Images on the Performance of Deep Neural Networks Based Diagnosis of COVID-19

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Title Effect of Pre-processing of CT Images on the Performance of Deep Neural Networks Based Diagnosis of COVID-19
 
Creator Luna, David Revelo
Mejía, Julio Eduardo
Chaves, Muñoz
 
Subject Convolutional neural networks
Deep learning
Entropy
Normalization
Transfer learning
 
Description 992-1000
COVID-19 disease is considered a new challenge around the world. Molecular testing is frequently used, aiming an early
detection. However, due to its complexity in the sampling protocol and delay diagnostic, it makes critical the time to
decisions on treatment or clinical interventions. In this work, the deep learning technique was adopted to evaluate the
performance of 4 systems based on convolutional neural networks (VGG16, VGG19, ResNet50, and MobileNet) to support
the diagnosis of COVID-19. CNN models were trained and tested using 340 CT images of patients diagnosed with COVID-
19, and the same numbers of images of patients without viruses, 1700 images were obtained for each class using
data-augmentation. On these images sets two types of pre-processing were performed normalization and entropy. The
parameters: accuracy, recall, precision, and F1Score were used as evaluation metrics. The study found that the best
performance in the classification of CT images of patients with COVID-19 was obtained by the MobileNet network with
normalization pre-processing attaining 98.04% accuracy. These findings suggest that the type of pre-processing influences
CNN's performance strongly. So as a guideline for future development, attention must be paid to implementing
pre-processing modules dedicated to highlighting the features of CT images image of COVID-19 positives cases to improve
the CNN performance.
 
Date 2021-11-17T05:33:38Z
2021-11-17T05:33:38Z
2021-11
 
Type Article
 
Identifier 0975-1084 (Online); 0022-4456 (Print)
http://nopr.niscair.res.in/handle/123456789/58523
 
Language en
 
Publisher CSIR-NIScPR
 
Source JSIR Vol.80(11) [November 2021]