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Study of Different Deep Learning Approach with Explainable AI for Screening Patients with COVID-19 Symptoms: Using CT Scan and Chest X-ray Image Dataset
The outbreak of COVID-19 disease caused more than 100,000 deaths so far ...
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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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Few-shot Learning for CT Scan based COVID-19 Diagnosis
Coronavirus disease 2019 (COVID-19) is a Public Health Emergency of Inte...
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Intra-model Variability in COVID-19 Classification Using Chest X-ray Images
X-ray and computed tomography (CT) scanning technologies for COVID-19 sc...
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Combining PCR and CT testing for COVID
We analyze the effect of using a screening CT-scan for evaluation of pot...
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Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT Imaging
With the increase of COVID-19 cases worldwide, an effective way is requi...
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Exploring Automatic COVID-19 Diagnosis via voice and symptoms from Crowdsourced Data
The development of fast and accurate screening tools, which could facili...
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Screening COVID-19 Based on CT/CXR Images Building a Publicly Available CT-scan Dataset of COVID-19
The rapid outbreak of COVID-19 threatens humans life all around the world. Due to insufficient diagnostic infrastructures, developing an accurate, efficient, inexpensive, and quick diagnostic tool is of great importance. As chest radiography, such as chest X-ray (CXR) and CT computed tomography (CT), is a possible way for screening COVID-19, developing an automatic image classification tool is immensely helpful for detecting the patients with COVID-19. To date, researchers have proposed several different screening methods; however, none of them could achieve a reliable and highly sensitive performance yet. The main drawbacks of current methods are the lack of having enough training data, low generalization performance, and a high rate of false-positive detection. To tackle such limitations, this study firstly builds a large-size publicly available CT-scan dataset, consisting of more than 13k CT-images of more than 1000 individuals, in which 8k images are taken from 500 patients infected with COVID-19. Secondly, we propose a deep learning model for screening COVID-19 using our proposed CT dataset and report the baseline results. Finally, we extend the proposed CT model for screening COVID-19 from CXR images using a transfer learning approach. The experimental results show that the proposed CT and CXR methods achieve the AUC scores of 0.886 and 0.984 respectively.
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