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Coronavirus (COVID-19) Classification using Deep Features Fusion and Ranking Technique
Coronavirus (COVID-19) emerged towards the end of 2019. World Health Org...
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Diagnosing COVID-19 Pneumonia from X-Ray and CT Images using Deep Learning and Transfer Learning Algorithms
COVID-19 (also known as 2019 Novel Coronavirus) first emerged in Wuhan, ...
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Detection of Covid-19 Patients with Convolutional Neural Network Based Features on Multi-class X-ray Chest Images
Covid-19 is a very serious deadly disease that has been announced as a p...
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A Light CNN for detecting COVID-19 from CT scans of the chest
OVID-19 is a world-wide disease that has been declared as a pandemic by ...
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Improving Automated COVID-19 Grading with Convolutional Neural Networks in Computed Tomography Scans: An Ablation Study
Amidst the ongoing pandemic, several studies have shown that COVID-19 cl...
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Coronavirus (COVID-19) Classification using CT Images by Machine Learning Methods
This study presents early phase detection of Coronavirus (COVID-19), whi...
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Triaging moderate COVID-19 and other viral pneumonias from routine blood tests
The COVID-19 is sweeping the world with deadly consequences. Its contagi...
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Classification of COVID-19 in Chest CT Images using Convolutional Support Vector Machines
Purpose: Coronavirus 2019 (COVID-19), which emerged in Wuhan, China and affected the whole world, has cost the lives of thousands of people. Manual diagnosis is inefficient due to the rapid spread of this virus. For this reason, automatic COVID-19 detection studies are carried out with the support of artificial intelligence algorithms. Methods: In this study, a deep learning model that detects COVID-19 cases with high performance is presented. The proposed method is defined as Convolutional Support Vector Machine (CSVM) and can automatically classify Computed Tomography (CT) images. Unlike the pre-trained Convolutional Neural Networks (CNN) trained with the transfer learning method, the CSVM model is trained as a scratch. To evaluate the performance of the CSVM method, the dataset is divided into two parts as training ( three different numbers of SVM kernels. Results: When the performance of pre-trained CNN networks and CSVM models is assessed, CSVM (7x7, 3x3, 1x1) model shows the highest performance with 94.03 92.19 Conclusion: The proposed method is more effective than other methods. It has proven in experiments performed to be an inspiration for combating COVID and for future studies.
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