ArticleFrontiers in medicine2022
Deep learning framework for prediction of infection severity of COVID-19.
Article in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Multi-Task NoisyViT for Enhanced Fruit and Vegetable Freshness Detection and Type Classification.Sensors (Basel, Switzerland) · 2025Article
- An Ensemble Deep Learning Approach for EEG-Based Emotion Recognition Using Multi-Class CSP.Biomimetics (Basel, Switzerland) · 2024Article
- Pulmonary Fissure Segmentation in CT Images Using Image Filtering and Machine Learning.Tomography (Ann Arbor, Mich.) · 2024Article
- Automatic Recognition of Multiple Emotional Classes from EEG Signals through the Use of Graph Theory and Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2024Article
- Accelerated algorithms for source orientation detection and spatiotemporal LCMV beamforming in EEG source localization.Frontiers in neuroscience · 2024Article
- A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022.Healthcare (Basel, Switzerland) · 2023Review
- A large-scale machine learning study of sociodemographic factors contributing to COVID-19 severity.Frontiers in big data · 2023Article
- Real-time deep-learning NICE classification and withdrawal-speed monitoring for colorectal endoscopy.Digital healthArticle
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the onset of the COVID-19 pandemic, quantifying the condition of positively diagnosed patients is of paramount importance. Chest CT scans can be used to measure the severity of a lung infection and the isolate involvement sites in order to increase awareness of a patient's disease progression. In this work, we developed a deep learning framework for lung infection severity prediction. To this end, we collected a dataset of 232 chest CT scans and involved two public datasets with an additional 59 scans for our model's training and used two external test sets with 21 scans for evaluation. On an input chest Computer Tomography (CT) scan, our framework, in parallel, performs a lung lobe segmentation utilizing a pre-trained model and infection segmentation using three distinct trained
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