Detection of COVID-19 from CT and Chest X-ray Images Using Deep Learning Models.

Coronavirus 2019 (COVID-19) is a highly transmissible and pathogenic virus caused by severe respiratory syndrome coronavirus 2 (SARS-CoV-2), which first appeared in Wuhan, China, and has since spread in the whole world. This pathology has caused a major health crisis in the world. However, the early detection of this anomaly is a key task to minimize their spread. Artificial intelligence is one of the approaches commonly used by researchers to discover the problems it causes and provide solutions. These estimates would help enable health systems to take the necessary steps to diagnose and track cases of COVID. In this review, we intend to offer a novel method of automatic detection of COVID-19 using tomographic images (CT) and radiographic images (Chest X-ray). In order to improve the performance of the detection system for this outbreak, we used two deep learning models: the VGG and ResNet. The results of the experiments show that our proposed models achieved the best accuracy of 99.35 and 96.77% respectively for VGG19 and ResNet50 with all the chest X-ray images.

COVID-19 detection in CT and CXR images using deep learning models.

Infectious diseases pose a threat to human life and could affect the whole world in a very short time. Corona-2019 virus disease (COVID-19) is an example of such harmful diseases. COVID-19 is a pandemic of an emerging infectious disease, called coronavirus disease 2019 or COVID-19, caused by the coronavirus SARS-CoV-2, which first appeared in December 2019 in Wuhan, China, before spreading around the world on a very large scale. The continued rise in the number of positive COVID-19 cases has disrupted the health care system in many countries, creating a lot of stress for governing bodies around the world, hence the need for a rapid way to identify cases of this disease. Medical imaging is a widely accepted technique for early detection and diagnosis of the disease which includes different techniques such as Chest X-ray (CXR), Computed Tomography (CT) scan, etc. In this paper, we propose a methodology to investigate the potential of deep transfer learning in building a classifier to detect COVID-19 positive patients using CT scan and CXR images. Data augmentation technique is used to increase the size of the training dataset in order to solve overfitting and enhance generalization ability of the model. Our contribution consists of a comprehensive evaluation of a series of pre-trained deep neural networks: ResNet50, InceptionV3, VGGNet-19, and Xception, using data augmentation technique. The findings proved that deep learning is effective at detecting COVID-19 cases. From the results of the experiments it was found that by considering each modality separately, the VGGNet-19 model outperforms the other three models proposed by using the CT image dataset where it achieved  88.5% precision, 86% recall, 86.5% F1-score, and 87% accuracy while the refined Xception version gave the highest precision, recall, F1-score, and accuracy values which equal 98% using CXR images dataset. On the other hand, and by applying the average of the two modalities X-ray and CT, VGG-19 presents the best score which is 90.5% for the accuracy and the F1-score, 90.3% for the recall while the precision is 91.5%. These results enables to automatize the process of analyzing chest CT scans and X-ray images with high accuracy and can be used in cases where RT-PCR testing and materials are limited.

Choriorétinopathie séreuse centrale bilatérale post COVID-19 Bilateral central serous chorioretinopathy post COVID-19

Résumé La maladie à virus corona (COVID-19) peut provoquer de nombreuses manifestations oculaires. Nous rapportons un cas rare de choriorétinopathie séreuse centrale bilatérale, post infection au COVID-19, chez une femme âgée de 38 ans qui s’est présentée pour un flou visuel bilatéral, 1 mois après l'infection au COVID-19. Elle avait de la fièvre, des frissons, une toux et un essoufflement avec fatigue et elle était positive au COVID-PCR. Pendant son séjour de 10 jours à l'hôpital, elle a reçu une oxygénothérapie, des antibiotiques, de l’héparine et des corticoïdes par voie intraveineuse puis relais par voie orale en ambulatoire. Après sa guérison du COVID-19, la patiente a développé une baisse progressive de l’acuité visuelle des 2 yeux: son acuité visuelle de loin corrigée était de 3/10 dans les 2 yeux, le segment antérieur était normal et le vitré était clair. L’examen du fond d’œil, complété par la tomographie en cohérence optique et l’angiographie à la fluorescéine a montré des décollements séreux rétiniens bilatéraux. L’évolution a été marquée par une amélioration de l'acuité visuelle et une régression des décollements séreux rétiniens. La choriorétinopathie séreuse centrale peut survenir après une infection au COVID-19 en raison de l'administration des corticoïdes et un contrôle ophtalmologique précoce est indispensable pour dépister au plus tôt une atteinte oculaire.

Detection Methods of COVID-19.

Since being first detected in China, coronavirus disease 2019 (COVID-19) has spread rapidly across the world, triggering a global pandemic with no viable cure in sight. As a result, national responses have focused on the effective minimization of the spread. Border control measures and travel restrictions have been implemented in a number of countries to limit the import and export of the virus. The detection of COVID-19 is a key task for physicians. The erroneous results of early laboratory tests and their delays led researchers to focus on different options. Information obtained from computed tomography (CT) and radiological images is important for clinical diagnosis. Therefore, it is worth developing a rapid method of detection of viral diseases through the analysis of radiographic images. We propose a novel method of detection of COVID-19. The purpose is to provide clinical decision support to healthcare workers and researchers. The article is to support researchers working on early detection of COVID-19 as well as similar viral diseases.