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COVID-CT-Dataset: A CT Scan Dataset about COVID-19
CT scans are promising in providing accurate, fast, and cheap screening ...
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MosMedData: Chest CT Scans With COVID-19 Related Findings Dataset
This dataset contains anonymised human lung computed tomography (CT) sca...
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Boosting Deep Transfer Learning for COVID-19 Classification
COVID-19 classification using chest Computed Tomography (CT) has been fo...
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Single-Shot Lightweight Model For The Detection of Lesions And The Prediction of COVID-19 From Chest CT Scans
We introduce a lightweight model based on Mask R-CNN with ResNet18 and R...
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Classification of COVID-19 in CT Scans using Multi-Source Transfer Learning
Since December of 2019, novel coronavirus disease COVID-19 has spread ar...
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COVID-CT-Mask-Net: Prediction of COVID-19 from CT Scans Using Regional Features
We present COVID-CT-Mask-Net model that predicts COVID-19 from CT scans....
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Automatic Calcium Scoring in Cardiac and Chest CT Using DenseRAUnet
Cardiovascular disease (CVD) is a common and strong threat to human bein...
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Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning
This paper explores how well deep learning models trained on chest CT images can diagnose COVID-19 infected people in a fast and automated process. To this end, we adopt advanced deep network architectures and propose a transfer learning strategy using custom-sized input tailored for each deep architecture to achieve the best performance. We conduct extensive sets of experiments on two CT image datasets, namely the SARS-CoV-2 CT-scan and the COVID19-CT. The obtained results show superior performances for our models compared with previous studies, where our best models achieve average accuracy, precision, sensitivity, specificity and F1 score of 99.4 on the SARS-CoV-2 dataset; and 92.9 COVID19-CT dataset, respectively. Furthermore, we apply two visualization techniques to provide visual explanations for the models' predictions. The visualizations show well-separated clusters for CT images of COVID-19 from other lung diseases, and accurate localizations of the COVID-19 associated regions.
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