Le nouveau Coronavirus Covid-19 : quels risques ophtalmiques ?

Hassani, Rachid Tahiri Joutei; Sandali, Otman

doi:10.1016/j.jfo.2020.02.001

Cited by 4 publications

(3 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, SVM was used to achieve better results. The number of COVID-19-infected patients and fatalities was investigated worldwide by Hassani [8] . A methodology for detecting the use of X-ray imaging (with the Vector Gadget Classifier) for Covid 19 infected patients has been recommended [9] .…”

Section: Background Studymentioning

confidence: 99%

Detail-Oriented Capsule Network for classification of CT scan images performing the detection of COVID-19

Modi¹,

Guhathakurta

Praveen

et al. 2023

Materials Today: Proceedings

View full text Add to dashboard Cite

COVID-19 is one of the biggest pandemics that the world is facing today, and every day, we are coming up with new challenges in this area. Still, much research is already going on to overcome this pandemic, and we also get succeeded to some extent. Diverse sources such as MRI, CT scanning, blood samples, X-ray image, and many more are available to detect COVID-19. Thus, it can be easily said that through image processing, the classification of COVID-19 can be done. In this study, the COVID-19 detection is done by classifying with the use of a type of convolutional neural network termed a detail-oriented capsule network. Chest CT scan imaging for the prediction of COVID-19 and non-COVID-19 are classified in the present paper using a Detailed Oriented capsule network (DOCN). Accuracy, specificity, and sensitivity are parameters used for model evaluation. The proposed model has achieved 98% accuracy, 81% sensitivity, and 98.4% specificity.

show abstract

Section: Background Studymentioning

confidence: 99%

Detail-Oriented Capsule Network for classification of CT scan images performing the detection of COVID-19

Modi¹,

Guhathakurta

Praveen

et al. 2023

Materials Today: Proceedings

View full text Add to dashboard Cite

show abstract

“…They found the model's accuracy to be 95.7%, while its sensitivity was at 90%, and the specificity at 95.8%. In Hassani and Sandali (2020), the author has discussed the total number of infections caused by COVID-19 globally and the case report regarding deaths associated with this virus. Ilyas et al (2020) discussed the different methods used to monitor diseases and challenges faced during its application.…”

Section: Literature Surveymentioning

confidence: 99%

“…Artificial intelligence has done a great job in COVID-19 detection through different machine learning and deep learning methods. In Lai et al (2020), Sun et al (2021), and Hassani and Sandali (2020), studies about using COVID-19 on kidney disease and kidney failure have been examined. According to Kawana et al (2020), patients who had cancer such as the COVID-19 disease have been classified into two recovery groups (i.e.…”

Section: Literature Surveymentioning

confidence: 99%

COVID-19 prediction through X-ray images using various layers in convolutional neural network

Mishra

Tiwari

Bajpai

et al. 2021

WJE

View full text Add to dashboard Cite

Purpose The purpose of this paper is to focus on the prediction of Coronavirus 2019 (COVID-19) using X-ray image. Design/methodology/approach This study proposed convolutional neural network (CNN) approach to predict COVID-19. Findings Prediction of COVID-19 using CNN. Originality/value The work has implemented multiple CNN models to classify chest X-ray of affected patients by using their chest scans. According to three models, the ResNet-50 is advantageous because of its high service reliability.

show abstract

Deep learning based detection and analysis of COVID-19 on chest X-ray images

et al. 2020

View full text Add to dashboard Cite

Covid-19 is a rapidly spreading viral disease that infects not only humans, but animals are also infected because of this disease. The daily life of human beings, their health, and the economy of a country are affected due to this deadly viral disease. Covid-19 is a common spreading disease, and till now, not a single country can prepare a vaccine for COVID-19. A clinical study of COVID-19 infected patients has shown that these types of patients are mostly infected from a lung infection after coming in contact with this disease. Chest x-ray (i.e., radiography) and chest CT are a more effective imaging technique for diagnosing lunge related problems. Still, a substantial chest x-ray is a lower cost process in comparison to chest CT. Deep learning is the most successful technique of machine learning, which provides useful analysis to study a large amount of chest x-ray images that can critically impact on screening of Covid-19. In this work, we have taken the PA view of chest x-ray scans for covid-19 affected patients as well as healthy patients. After cleaning up the images and applying data augmentation, we have used deep learningbased CNN models and compared their performance. We have compared Inception V3, Xception, and ResNeXt models and examined their accuracy. To analyze the model performance, 6432 chest x-ray scans samples have been collected from the Kaggle repository, out of which 5467 were used for training and 965 for validation. In result analysis, the Xception model gives the highest accuracy (i.e., 97.97%) for detecting Chest X-rays images as compared to other models. This work only focuses on possible methods of classifying covid-19 infected patients and does not claim any medical accuracy.

show abstract

Le nouveau Coronavirus Covid-19 : quels risques ophtalmiques ?

Cited by 4 publications

References 4 publications

Detail-Oriented Capsule Network for classification of CT scan images performing the detection of COVID-19

Detail-Oriented Capsule Network for classification of CT scan images performing the detection of COVID-19

COVID-19 prediction through X-ray images using various layers in convolutional neural network

Deep learning based detection and analysis of COVID-19 on chest X-ray images

Contact Info

Product

Resources

About