ArticleComputational intelligence and neuroscience2022
Multiclass Classification of Chest X-Ray Images for the Prediction of COVID-19 Using Capsule Network.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- DeepChestGNN: A Comprehensive Framework for Enhanced Lung Disease Identification through Advanced Graphical Deep Features.Sensors (Basel, Switzerland) · 2024Article
- MTMC-AUR2CNet: Multi-textural multi-class attention recurrent residual convolutional neural network for COVID-19 classification using chest X-ray images.Biomedical signal processing and control · 2023Article
- Artificial Intelligence Based COVID-19 Detection and Classification Model on Chest X-ray Images.Healthcare (Basel, Switzerland) · 2023Article
- COVID-SegNet: encoder-decoder-based architecture for COVID-19 lesion segmentation in chest X-ray.Multimedia systems · 2023Article
- Retracted: Multiclass Classification of Chest X-Ray Images for the Prediction of COVID-19 Using Capsule Network.Computational intelligence and neuroscience · 2023Article
- Equilibrium Optimization Algorithm with Ensemble Learning Based Cervical Precancerous Lesion Classification Model.Healthcare (Basel, Switzerland) · 2022Article
- COVID-DSNet: A novel deep convolutional neural network for detection of coronavirus (SARS-CoV-2) cases from CT and Chest X-Ray images.Artificial intelligence in medicine · 2022Article
- Explanatory classification of CXR images into COVID-19, Pneumonia and Tuberculosis using deep learning and XAI.Computers in biology and medicine · 2022Article
- A Comprehensive Review of Machine Learning Used to Combat COVID-19.Diagnostics (Basel, Switzerland) · 2022Review
- Enhanced Gravitational Search Optimization with Hybrid Deep Learning Model for COVID-19 Diagnosis on Epidemiology Data.Healthcare (Basel, Switzerland) · 2022Article
- A Novel Method for COVID-19 Detection Based on DCNNs and Hierarchical Structure.Computational and mathematical methods in medicine · 2022Article
- Automated COVID-19 Classification Using Heap-Based Optimization with the Deep Transfer Learning Model.Computational intelligence and neuroscience · 2022Article
Corrections and comments
- Retraction · 2023-12-13Compromised Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Paper Mill · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
It is critical to establish a reliable method for detecting people infected with COVID-19 since the pandemic has numerous harmful consequences worldwide. If the patient is infected with COVID-19, a chest X-ray can be used to determine this. In this work, an X-ray showing a COVID-19 infection is classified by the capsule neural network model we trained to recognise. 6310 chest X-ray pictures were used to train the models, separated into three categories: normal, pneumonia, and COVID-19. This work is considered an improved deep learning model for the classification of COVID-19 disease through X-ray images. Viewpoint invariance, fewer parameters, and better generalisation are some of the advantages of CapsNet compared with the classic convolutional neural network (CNN) models. The proposed model has achieved an accuracy greater than 95% during the model's training, which is better than the other state-of-the-art algorithms. Furthermore, to aid in detecting COVID-19 in a chest X-ray, the model could provide extra information.
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Registered trials
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