ArticleMultimedia tools and applications2023
LWSNet - a novel deep-learning architecture to segregate Covid-19 and pneumonia from x-ray imagery.
Article in Multimedia tools and applications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 26 citations in OpenAlex.
- RETRACTED: LGD_Net: Capsule network with extreme learning machine for classification of lung diseases using CT scans.PloS one · 2025Article
- SqueezeViX-Net with SOAE: A Prevailing Deep Learning Framework for Accurate Pneumonia Classification using X-Ray and CT Imaging Modalities.Current medical imaging · 2025Article
- Deep Learning for Pneumonia Detection in Chest X-ray Images: A Comprehensive Survey.Journal of imaging · 2024Review
- AI-powered COVID-19 forecasting: a comprehensive comparison of advanced deep learning methods.Osong public health and research perspectives · 2024Article
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Authors and funding
5 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
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.
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