Covid-19 ct lung image segmentation using adaptive donkey and smuggler optimization algorithm
- Title
- Covid-19 ct lung image segmentation using adaptive donkey and smuggler optimization algorithm
- Creator
- Prabu P.; Venkatachalam K.; Alluhaidan A.S.; Marzouk R.; Hadjouni M.; El Rahman S.A.
- Description
- 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.
- Source
- Computers, Materials and Continua, Vol-71, No. 1, pp. 1133-1152.
- Date
- 2022-01-01
- Publisher
- Tech Science Press
- Subject
- Adaptive donkey and snuggler optimization.bi-directional long short termmemory; Coronavirus disease 2019; Randomly selected propagation; Semi-supervised learning
- Coverage
- 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
- Rights
- All Open Access; Gold Open Access
- Relation
- ISSN: 15462218
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Prabu P.; Venkatachalam K.; Alluhaidan A.S.; Marzouk R.; Hadjouni M.; El Rahman S.A., “Covid-19 ct lung image segmentation using adaptive donkey and smuggler optimization algorithm,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/15458.