A Light Deep Learning Algorithm for CT Diagnosis of COVID-19 Pneumonia

A large number of reports present artificial intelligence (AI) algorithms, which support pneumonia detection caused by COVID-19 from chest CT (computed tomography) scans. Only a few studies provided access to the source code, which limits the analysis of the out-of-distribution generalization abilit...

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Bibliographic Details
Published in:Diagnostics (Basel) 2022-06, Vol.12 (7), p.1527
Main Authors: Furtado, Adhvan, da Purificação, Carlos Alberto Campos, Badaró, Roberto, Nascimento, Erick Giovani Sperandio
Format: Article
Language:English
Subjects:
Algorithms
Clinical medicine
Coronaviruses
COVID-19
Deep learning
Lungs
Medical imaging
Patients
Pneumonia
Respiratory diseases
screening test
Severe acute respiratory syndrome coronavirus 2
X-rays
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Summary:A large number of reports present artificial intelligence (AI) algorithms, which support pneumonia detection caused by COVID-19 from chest CT (computed tomography) scans. Only a few studies provided access to the source code, which limits the analysis of the out-of-distribution generalization ability. This study presents Cimatec-CovNet-19, a new light 3D convolutional neural network inspired by the VGG16 architecture that supports COVID-19 identification from chest CT scans. We trained the algorithm with a dataset of 3000 CT Scans (1500 COVID-19-positive) with images from different parts of the world, enhanced with 3000 images obtained with data augmentation techniques. We introduced a novel pre-processing approach to perform a slice-wise selection based solely on the lung CT masks and an empirically chosen threshold for the very first slice. It required only 16 slices from a CT examination to identify COVID-19. The model achieved a recall of 0.88, specificity of 0.88, ROC-AUC of 0.95, PR-AUC of 0.95, and F1-score of 0.88 on a test set with 414 samples (207 COVID-19). These results support Cimatec-CovNet-19 as a good and light screening tool for COVID-19 patients. The whole code is freely available for the scientific community.
ISSN:2075-4418
2075-4418
DOI:10.3390/diagnostics12071527