ArticleDisease markers2021
Prediction of COVID-19 with Computed Tomography Images using Hybrid Learning Techniques.
Article in Disease markers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Role of Artificial Intelligence in COVID-19 Detection.Sensors (Basel, Switzerland) · 2021Pooled it
- Classification of Severity of Lung Parenchyma Using Saliency and Discrete Cosine Transform Energy in Computed Tomography of Patients With COVID-19.International journal of telemedicine and applications · 2025Article
- Learning without forgetting by leveraging transfer learning for detecting COVID-19 infection from CT images.Scientific reports · 2023Article
- Deep-Learning-Based COVID-19 Diagnosis and Implementation in Embedded Edge-Computing Device.Diagnostics (Basel, Switzerland) · 2023Article
- Using fused Contourlet transform and neural features to spot COVID19 infections in CT scan images.Intelligent systems with applications · 2023Article
- Combined Cloud-Based Inference System for the Classification of COVID-19 in CT-Scan and X-Ray Images.New generation computing · 2023Article
- Review and classification of AI-enabled COVID-19 CT imaging models based on computer vision tasks.Computers in biology and medicine · 2022Review
- Detection of COVID-19 findings by the local interpretable model-agnostic explanations method of types-based activations extracted from CNNs.Biomedical signal processing and control · 2022Article
- CVD-HNet: Classifying Pneumonia and COVID-19 in Chest X-ray Images Using Deep Network.Wireless personal communications · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Reverse Transcription Polymerase Chain Reaction (RT-PCR) used for diagnosing COVID-19 has been found to give low detection rate during early stages of infection. Radiological analysis of CT images has given higher prediction rate when compared to RT-PCR technique. In this paper, hybrid learning models are used to classify COVID-19 CT images, Community-Acquired Pneumonia (CAP) CT images, and normal CT images with high specificity and sensitivity. The proposed system in this paper has been compared with various machine learning classifiers and other deep learning classifiers for better data analysis. The outcome of this study is also compared with other studies which were carried out recently on COVID-19 classification for further analysis. The proposed model has been found to outperform with an accuracy of 96.69%, sensitivity of 96%, and specificity of 98%.
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Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.