LWSNet-a novel deep-learning architecture to segregate Covid-19 and pneumonia from x-ray imagery
| dc.contributor.author | Lasker A.;Ghosh M.;Obaidullah S.M.;Chakraborty C.;Roy K. | |
| dc.date.accessioned | 2025-05-09T10:02:27Z | |
| dc.date.available | 2025-05-09T10:02:27Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | Automatic detection of lung diseases using AI-based tools became very much necessary to handle the huge number of cases occurring across the globe and support the doctors. This paper proposed a novel deep learning architecture named LWSNet (Light Weight Stacking Network) to separate Covid-19, cold pneumonia, and normal chest x-ray images. This framework is based on single, double, triple, and quadruple stack mechanisms to address the above-mentioned tri-class problem. In this framework, a truncated version of standard deep learning models and a lightweight CNN model was considered to conviniently deploy in resource-constraint devices. An evaluation was conducted on three publicly available datasets alongwith their combination. We received 97.28%, 96.50%, 97.41%, and 98.54% highest classification accuracies using quadruple stack. On further investigation, we found, using LWSNet, the average accuracy got improved from individual model to quadruple model by 2.31%, 2.55%, 2.88%, and 2.26% on four respective datasets. | |
| dc.identifier.uri | https://link.springer.com/article/10.1007/s11042-022-14247-3 | |
| dc.identifier.uri | http://ssm.ndl.gov.in/handle/123456789/1175 | |
| dc.language.iso | en | |
| dc.publisher | Multimedia Tools and Applications | |
| dc.title | LWSNet-a novel deep-learning architecture to segregate Covid-19 and pneumonia from x-ray imagery | |
| dc.type | Article |