ArticleChaos, solitons, and fractals2021
TLCoV- An automated Covid-19 screening model using Transfer Learning from chest X-ray images.
Article in Chaos, solitons, and fractals, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- A 19-layer convolutional neural network for accurate COVID-19 detection in chest X-ray images: comparative analysis with pretrained networks.Scientific reports · 2026Article
- Unlocking the Power of 3D Convolutional Neural Networks for COVID-19 Detection: A Comprehensive Review.Journal of imaging informatics in medicine · 2025Review
- A tailored deep learning approach for early detection of oral cancer using a 19-layer CNN on clinical lip and tongue images.Scientific reports · 2025Article
- A comprehensive review of analyzing the chest X-ray images to detect COVID-19 infections using deep learning techniques.Soft computing · 2023Article
- IRCM-Caps: An X-ray image detection method for COVID-19.The clinical respiratory journal · 2023Article
- Investigation of optimal convolutional neural network conditions for thyroid ultrasound image analysis.Scientific reports · 2023Article
- Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment.Sensors (Basel, Switzerland) · 2023Review
- A MobileNet-based CNN model with a novel fine-tuning mechanism for COVID-19 infection detection.Soft computing · 2023Article
- Preliminary Stages for COVID-19 Detection Using Image Processing.Diagnostics (Basel, Switzerland) · 2022Review
- Cloud-based COVID-19 disease prediction system from X-Ray images using convolutional neural network on smartphone.Multimedia tools and applications · 2022Article
- Automated Lung-Related Pneumonia and COVID-19 Detection Based on Novel Feature Extraction Framework and Vision Transformer Approaches Using Chest X-ray Images.Bioengineering (Basel, Switzerland) · 2022Article
- RESCOVIDTCNnet: A residual neural network-based framework for COVID-19 detection using TCN and EWT with chest X-ray images.Expert systems with applications · 2022Article
- BLCov: A novel collaborative-competitive broad learning system for COVID-19 detection from radiology images.Engineering applications of artificial intelligence · 2022Article
- A lightweight CNN-based network on COVID-19 detection using X-ray and CT images.Computers in biology and medicine · 2022Article
- COVID-19 diagnosis using state-of-the-art CNN architecture features and Bayesian Optimization.Computers in biology and medicine · 2022Review
- Towards edge devices implementation: deep learning model with visualization for COVID-19 prediction from chest X-ray.Advances in computational intelligence · 2022Article
- Transfer learning with fine-tuned deep CNN ResNet50 model for classifying COVID-19 from chest X-ray images.Informatics in medicine unlocked · 2022Article
- Deep learning empowered COVID-19 diagnosis using chest CT scan images for collaborative edge-cloud computing platform.Multimedia tools and applications · 2022Article
- A deep learning-based framework for detecting COVID-19 patients using chest X-rays.Multimedia systems · 2022Article
- A Hybrid Convolutional Neural Network Model for Diagnosis of COVID-19 Using Chest X-ray Images.International journal of environmental research and public health · 2021Article
Corrections and comments
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The Coronavirus disease (Covid-19) has been declared a pandemic by World Health Organisation (WHO) and till date caused 585,727 numbers of deaths all over the world. The only way to minimize the number of death is to quarantine the patients tested Corona positive. The quick spread of this disease can be reduced by automatic screening to cover the lack of radiologists. Though the researchers already have done extremely well to design pioneering deep learning models for the screening of Covid-19, most of them results in low accuracy rate. In addition, over-fitting problem increases difficulties for those models to learn on existing Covid-19 datasets. In this paper, an automated Covid-19 screening model is designed to identify the patients suffering from this disease by using their chest X-ray images. The model classifies the images in three categories - Covid-19 positive, other pneumonia infection and no infection. Three learning schemes such as CNN, VGG-16 and ResNet-50 are separately used to learn the model. A standard Covid-19 radiography dataset from the repository of Kaggle is used to get the chest X-ray images. The performance of the model with all the three learning schemes has been evaluated and it shows VGG-16 performed better as compared to CNN and ResNet-50. The model with VGG-16 gives the accuracy of 97.67%, precision of 96.65%, recall of 96.54% and F1 score of 96.59%. The performance evaluation also shows that our model outperforms two existing models to screen the Covid-19.
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