A Light Deep Learning Algorithm for CT Diagnosis of COVID-19 Pneumonia.
Adhvan FurtadoCarlos Alberto Campos da PurificaçãoRoberto José da Silva BadaróErick Giovani Sperandio NascimentoPublished in: Diagnostics (Basel, Switzerland) (2022)
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.
Keyphrases
- deep learning
- computed tomography
- dual energy
- coronavirus disease
- artificial intelligence
- sars cov
- image quality
- convolutional neural network
- contrast enhanced
- machine learning
- positron emission tomography
- magnetic resonance imaging
- big data
- mental health
- healthcare
- respiratory syndrome coronavirus
- optical coherence tomography
- emergency department
- resistance training
- body composition
- quantum dots
- extracorporeal membrane oxygenation
- adverse drug