Within the lack of chest COVID-19 X-ray dataset: A novel detection model based on GAN and deep transfer learning
TLDR
The main idea is to collect all the possible images for COVID-19 that exists until the writing of this research and use the GAN network to generate more images to help in the detection of this virus from the available X-rays images with the highest accuracy possible.Abstract:
The coronavirus (COVID-19) pandemic is putting healthcare systems across the world under unprecedented and increasing pressure according to the World Health Organization (WHO). With the advances in computer algorithms and especially Artificial Intelligence, the detection of this type of virus in the early stages will help in fast recovery and help in releasing the pressure off healthcare systems. In this paper, a GAN with deep transfer learning for coronavirus detection in chest X-ray images is presented. The lack of datasets for COVID-19 especially in chest X-rays images is the main motivation of this scientific study. The main idea is to collect all the possible images for COVID-19 that exists until the writing of this research and use the GAN network to generate more images to help in the detection of this virus from the available X-rays images with the highest accuracy possible. The dataset used in this research was collected from different sources and it is available for researchers to download and use it. The number of images in the collected dataset is 307 images for four different types of classes. The classes are the COVID-19, normal, pneumonia bacterial, and pneumonia virus. Three deep transfer models are selected in this research for investigation. The models are the Alexnet, Googlenet, and Restnet18. Those models are selected for investigation through this research as it contains a small number of layers on their architectures, this will result in reducing the complexity, the consumed memory and the execution time for the proposed model. Three case scenarios are tested through the paper, the first scenario includes four classes from the dataset, while the second scenario includes 3 classes and the third scenario includes two classes. All the scenarios include the COVID-19 class as it is the main target of this research to be detected. In the first scenario, the Googlenet is selected to be the main deep transfer model as it achieves 80.6% in testing accuracy. In the second scenario, the Alexnet is selected to be the main deep transfer model as it achieves 85.2% in testing accuracy, while in the third scenario which includes two classes (COVID-19, and normal), Googlenet is selected to be the main deep transfer model as it achieves 100% in testing accuracy and 99.9% in the validation accuracy. All the performance measurement strengthens the obtained results through the research.read more
Citations
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Journal ArticleDOI
A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic
Mohamed Loey,Gunasekaran Manogaran,Gunasekaran Manogaran,Mohamed Hamed N. Taha,Nour Eldeen M. Khalifa +4 more
TL;DR: A hybrid model using deep and classical machine learning for face mask detection will be presented, and the SVM classifier achieved 99.64 % testing accuracy in RMFD.
Journal ArticleDOI
A Combined Deep CNN-LSTM Network for the Detection of Novel Coronavirus (COVID-19) Using X-ray Images
TL;DR: This paper aims to introduce a deep learning technique based on the combination of a convolutional neural network (CNN) and long short-term memory (LSTM) to diagnose COVID-19 automatically from X-ray images, which achieved desired results on the currently available dataset.
Journal ArticleDOI
Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation.
TL;DR: An automatic classification segmentation tool for helping screening COVID-19 pneumonia using chest CT imaging and shows very encouraging performance with a dice coefficient higher than 0.88 for the segmentation and an area under the ROC curve higher than 97% for the classification.
Journal ArticleDOI
Fighting against COVID-19: A novel deep learning model based on YOLO-v2 with ResNet-50 for medical face mask detection.
Mohamed Loey,Gunasekaran Manogaran,Gunasekaran Manogaran,Mohamed Hamed N. Taha,Nour Eldeen M. Khalifa +4 more
TL;DR: The objective of this paper is to annotate and localize the medical face mask objects in real-life images to improve the object detection process and it is concluded that the adam optimizer achieved the highest average precision percentage of 81% as a detector.
Journal ArticleDOI
Covid-19 Classification by FGCNet with Deep Feature Fusion from Graph Convolutional Network and Convolutional Neural Network.
Shuihua Wang,Shuihua Wang,Shuihua Wang,Vishnuvarthanan Govindaraj,Juan Manuel Górriz,Juan Manuel Górriz,Xin Zhang,Yudong Zhang,Yudong Zhang +8 more
TL;DR: The proposed FGCNet model can assist radiologists to rapidly detect COVID-19 from chest CT images and gives better performance than all 15 state-of-the-art methods.
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