Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Kernel granulometric texture analysis and light res-aspp-unet classification for covid-19 detection Subject The topic of the resource Convolutional neural network; COVID-19; Deep residual learning; Granulo metrics texture analysis; Principal component analysis; X-ray Description An account of the resource This research article proposes an automatic frame work for detecting COVID -19 at the early stage using chest X-ray image. It is an undeniable fact that coronovirus is a serious disease but the early detection of the virus present in human bodies can save lives. In recent times, there are somany research solutions that have been presented for early detection, but there is still a lack in need of right and even rich technology for its early detection. The proposed deep learning model analysis the pixels of every image and adjudges the presence of virus. The classifier is designed in such a way so that, it automatically detects the virus present in lungs using chest image. This approach uses an image texture analysis technique called granulometric mathematical model. Selected features are heuristically processed for optimization using novel multi scaling deep learning called light weight residual-atrous spatial pyramid pooling (LightRES-ASPP-Unet) Unet model. The proposed deep LightRES-ASPPUnet technique has a higher level of contracting solution by extracting major level of image features. Moreover, the corona virus has been detected using high resolution output. In the framework, atrous spatial pyramid pooling (ASPP) method is employed at its bottom level for incorporating the deep multi scale features in to the discriminative mode. The architectural working starts from the selecting the features from the image using granulometric mathematical model and the selected features are optimized using LightRESASPP- Unet. ASPP in the analysis of images has performed better than the existing Unet model. The proposed algorithm has achieved 99.6% of accuracy in detecting the virus at its early stage. 2022 Tech Science Press. All rights reserved. Creator An entity primarily responsible for making the resource Devipriya A.; Prabu P.; Venkatachalam K.; Ibrahim A.Z. Source A related resource from which the described resource is derived Computers, Materials and Continua, Vol-71, No. 1, pp. 651-666. Publisher An entity responsible for making the resource available Tech Science Press Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.32604/cmc.2022.020820" target="_blank" rel="noreferrer noopener">https://doi.org/10.32604/cmc.2022.020820</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118540112&amp;doi=10.32604%2Fcmc.2022.020820&amp;partnerID=40&amp;md5=fc1753c6e8636b1d0160c310c01a3805" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118540112&amp;doi=10.32604%2fcmc.2022.020820&amp;partnerID=40&amp;md5=fc1753c6e8636b1d0160c310c01a3805</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 15462218 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Devipriya A., Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Coimbatore, 641407, India; Prabu P., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, 560029, India; Venkatachalam K., Department of Applied Cybernetics, Faculty of Science, University of Hradec Kralove, Hradec Kralove, 50003, Czech Republic; Ibrahim A.Z., Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, KSA.P.o.Box: 84428, Riyadh, 11671, Saudi Arabia