LWSNet - a novel deep-learning architecture to segregate Covid-19 and pneumonia from x-ray imagery
- 3 December 2022
- journal article
- research article
- Published by Springer Science and Business Media LLC in Multimedia Tools and Applications
- Vol. 82 (14), 21801-21823
- https://doi.org/10.1007/s11042-022-14247-3
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.Keywords
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