ArticleSensors (Basel, Switzerland)2021
Recognition of COVID-19 from CT Scans Using Two-Stage Deep-Learning-Based Approach: CNR-IEMN.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 24 citations in OpenAlex.
- Reinvestigating the performance of artificial intelligence classification algorithms on COVID-19 X-Ray and CT images.iScience · 2024Article
- EffViT-COVID: A dual-path network for COVID-19 percentage estimation.Expert systems with applications · 2023Article
- Omnidirectional 2.5D representation for COVID-19 diagnosis using chest CTs.Journal of visual communication and image representation · 2023Article
- A privacy-aware method for COVID-19 detection in chest CT images using lightweight deep conventional neural network and blockchain.Computers in biology and medicine · 2022Article
- Fully automatic pipeline of convolutional neural networks and capsule networks to distinguish COVID-19 from community-acquired pneumonia via CT images.Computers in biology and medicine · 2022Article
- Per-COVID-19: A Benchmark Dataset for COVID-19 Percentage Estimation from CT-Scans.Journal of imaging · 2021Article
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4 authors at 3 institutions in 2 countries.
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Abstract
Since the appearance of the COVID-19 pandemic (at the end of 2019, Wuhan, China), the recognition of COVID-19 with medical imaging has become an active research topic for the machine learning and computer vision community. This paper is based on the results obtained from the 2021 COVID-19 SPGC challenge, which aims to classify volumetric CT scans into normal, COVID-19, or community-acquired pneumonia (Cap) classes. To this end, we proposed a deep-learning-based approach (CNR-IEMN) that consists of two main stages. In the first stage, we trained four deep learning architectures with a multi-tasks strategy for slice-level classification. In the second stage, we used the previously trained models with an XG-boost classifier to classify the whole CT scan into normal, COVID-19, or Cap classes. Our approach achieved a good result on the validation set, with an overall accuracy of 87.75% and 96.36%, 52.63%, and 95.83% sensitivities for COVID-19, Cap, and normal, respectively. On the other hand, our approach achieved fifth place on the three test datasets of SPGC in the COVID-19 challenge, where our approach achieved the best result for COVID-19 sensitivity. In addition, our approach achieved second place on two of the three testing sets.
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