ArticlePattern recognition letters2021
Unsupervised Deep Learning based Variational Autoencoder Model for COVID-19 Diagnosis and Classification.
Article in Pattern recognition letters, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Deep Learning in Digital Breast Tomosynthesis: Current Status, Challenges, and Future Trends.MedComm · 2025Review
- A GPT-based EHR modeling system for unsupervised novel disease detection.Journal of biomedical informatics · 2024Article
- A comprehensive review of COVID-19 detection with machine learning and deep learning techniques.Health and technology · 2023Article
- Automatic Classification of Magnetic Resonance Histology of Peripheral Arterial Chronic Total Occlusions Using a Variational Autoencoder: A Feasibility Study.Diagnostics (Basel, Switzerland) · 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
- MIC-Net: A deep network for cross-site segmentation of COVID-19 infection in the fog-assisted IoMT.Information sciences · 2023Article
- Enhancing the Breast Histopathology Image Analysis for Cancer Detection Using Variational Autoencoder.International journal of environmental research and public health · 2023Article
- Systematic review of MCDM approach applied to the medical case studies of COVID-19: trends, bibliographic analysis, challenges, motivations, recommendations, and future directions.Complex & intelligent systems · 2023Article
- Enhanced framework for COVID-19 prediction with computed tomography scan images using dense convolutional neural network and novel loss function.Computers & electrical engineering : an international journal · 2023Article
- The role of machine learning in health policies during the COVID-19 pandemic and in long COVID management.Frontiers in public health · 2023Review
- How much BiGAN and CycleGAN-learned hidden features are effective for COVID-19 detection from CT images? A comparative study.The Journal of supercomputing · 2023Article
- Interpretable tourism demand forecasting with temporal fusion transformers amid COVID-19.Applied intelligence (Dordrecht, Netherlands) · 2023Article
- 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
- EVAE-Net: An Ensemble Variational Autoencoder Deep Learning Network for COVID-19 Classification Based on Chest X-ray Images.Diagnostics (Basel, Switzerland) · 2022Article
- Unsupervised Outlier Detection in IOT Using Deep VAE.Sensors (Basel, Switzerland) · 2022Article
- Enhanced Gravitational Search Optimization with Hybrid Deep Learning Model for COVID-19 Diagnosis on Epidemiology Data.Healthcare (Basel, Switzerland) · 2022Article
- Leveraging Tweets for Artificial Intelligence Driven Sentiment Analysis on the COVID-19 Pandemic.Healthcare (Basel, Switzerland) · 2022Article
- Quantum OPTICS and deep self-learning on swarm intelligence algorithms for Covid-19 emergency transportation.Soft computing · 2022Article
- Machine learning applications for COVID-19 outbreak management.Neural computing & applications · 2022Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
At present times, COVID-19 has become a global illness and infected people has increased exponentially and it is difficult to control due to the non-availability of large quantity of testing kits. Artificial intelligence (AI) techniques including machine learning (ML), deep learning (DL), and computer vision (CV) approaches find useful for the recognition, analysis, and prediction of COVID-19. Several ML and DL techniques are trained to resolve the supervised learning issue. At the same time, the potential measure of the unsupervised learning technique is quite high. Therefore, unsupervised learning techniques can be designed in the existing DL models for proficient COVID-19 prediction. In this view, this paper introduces a novel unsupervised DL based variational autoencoder (UDL-VAE) model for COVID-19 detection and classification. The UDL-VAE model involved adaptive Wiener filtering (AWF) based preprocessing technique to enhance the image quality. Besides, Inception v4 with Adagrad technique is employed as a feature extractor and unsupervised VAE model is applied for the classification process. In order to verify the superior diagnostic performance of the UDL-VAE model, a set of experimentation was carried out to highlight the effective outcome of the UDL-VAE model. The obtained experimental values showcased the effectual results of the UDL-VAE model with the higher accuracy of 0.987 and 0.992 on the binary and multiple classes respectively.
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