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 Covid-19 ct lung image segmentation using adaptive donkey and smuggler optimization algorithm Subject The topic of the resource Adaptive donkey and snuggler optimization.bi-directional long short termmemory; Coronavirus disease 2019; Randomly selected propagation; Semi-supervised learning Description An account of the resource COVID'19 has caused the entire universe to be in existential health crisis by spreading globally in the year 2020. The lungs infection is detected in Computed Tomography (CT) images which provide the best way to increase the existing healthcare schemes in preventing the deadly virus. Nevertheless, separating the infected areas in CT images faces various issues such as lowintensity difference among normal and infectious tissue and high changes in the characteristics of the infection. To resolve these issues, a newinf-Net (Lung Infection Segmentation Deep Network) is designed for detecting the affected areas from the CT images automatically. For the worst segmentation results, the Edge-Attention Representation (EAR) is optimized using Adaptive Donkey and Smuggler Optimization (ADSO). The edges which are identified by the ADSO approach is utilized for calculating dissimilarities. An IFCM (Intuitionistic Fuzzy C-Means) clustering approach is applied for computing the similarity of the EA component among the generated edge maps and Ground-Truth (GT) edge maps. Also, a Semi-Supervised Segmentation (SSS) structure is designed using the Randomly Selected Propagation (RP) technique and Inf-Net, which needs only less number of images and unlabelled data. Semi-Supervised Multi-Class Segmentation (SSMCS) is designed using a Bi-LSTM (Bi-Directional Long-Short-Term-memory), acquires all the advantages of the disease segmentation done using Semi Inf-Net and enhances the execution of multi-class disease labelling. The newly designed SSMCS approach is compared with existing U-Net++, MCS, and Semi-Inf-Net. factors such as MAE (Mean Absolute Error), Structure measure, Specificity (Spec), Dice Similarity coefficient, Sensitivity (Sen), and Enhance-Alignment Measure are considered for evaluation purpose. 2022 Tech Science Press. All rights reserved. Creator An entity primarily responsible for making the resource Prabu P.; Venkatachalam K.; Alluhaidan A.S.; Marzouk R.; Hadjouni M.; El Rahman S.A. Source A related resource from which the described resource is derived Computers, Materials and Continua, Vol-71, No. 1, pp. 1133-1152. 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.2022.020919" target="_blank" rel="noreferrer noopener">https://doi.org/10.32604/cmc.2022.020919</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118586359&amp;doi=10.32604%2Fcmc.2022.020919&amp;partnerID=40&amp;md5=9bec7980f3290a663c58fcf48a47596e" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118586359&amp;doi=10.32604%2fcmc.2022.020919&amp;partnerID=40&amp;md5=9bec7980f3290a663c58fcf48a47596e</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 Prabu P., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India; Venkatachalam K., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bengaluru, India; Alluhaidan A.S., Information Systems Department, College of Computer and Information Science, Princess Nourah Bint Abdulrahman University, Riyadh, 84428, Saudi Arabia; Marzouk R., Department of Mathematics, Faculty of Science, Cairo University, Giza, 12613, Egypt; Hadjouni M., Computer Sciences Department, College of Computer and Information Science, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia; El Rahman S.A., Computer Sciences Department, College of Computer and Information Science, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia, Department of Electrical Engineering, Faculty of Engineering-Shoubra, Benha University, Cairo, Egypt