ArticleMaterials today. Proceedings2023
Detail-Oriented Capsule Network for classification of CT scan images performing the detection of COVID-19.
Article in Materials today. Proceedings, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It carries an expression of concern. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Application of Deep Learning Techniques in Diagnosis of Covid-19 (Coronavirus): A Systematic Review.Neural processing letters · 2022Review
Corrections and comments
- Expression of concern
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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