ArticleDiagnostics (Basel, Switzerland)2022
A Light Deep Learning Algorithm for CT Diagnosis of COVID-19 Pneumonia.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Novel large empirical study of deep transfer learning for COVID-19 classification based on CT and X-ray images.Scientific reports · 2024Article
- Detection method of viral pneumonia imaging features based on CT scan images in COVID-19 case study.MethodsX · 2024Article
- COVID-19 detection in lung CT slices using Brownian-butterfly-algorithm optimized lightweight deep features.Heliyon · 2024Article
- RETRACTED: Multi-modal deep learning methods for classification of chest diseases using different medical imaging and cough sounds.PloS one · 2024Article
- Remote Heart Rate Prediction in Virtual Reality Head-Mounted Displays Using Machine Learning Techniques.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
4 authors.
Funding
Abstract
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.
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