ArticleJournal of ambient intelligence and humanized computing2023
Novel deep transfer learning model for COVID-19 patient detection using X-ray chest images.
Article in Journal of ambient intelligence and humanized computing, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed.
- Viral pneumonia detection during the COVID-19 pandemic using deep learning and DCGAN-based data augmentation.Scientific reports · 2026Article
- TL-PneuNet: a transfer learning-based pneumonia classification framework.Scientific reports · 2025Article
- FACNN: fuzzy-based adaptive convolution neural network for classifying COVID-19 in noisy CXR images.Medical & biological engineering & computing · 2024Article
- Detailed-based dictionary learning for low-light image enhancement using camera response model for industrial applications.Scientific reports · 2024Article
- Article
- A brief review and scientometric analysis on ensemble learning methods for handling COVID-19.Heliyon · 2024Article
- Deep learning based suture training system.Surgery open science · 2023Article
- Deep Learning-Based Classification of Chest Diseases Using X-rays, CT Scans, and Cough Sound Images.Diagnostics (Basel, Switzerland) · 2023Article
- Optimized Xception Learning Model and XgBoost Classifier for Detection of Multiclass Chest Disease from X-ray Images.Diagnostics (Basel, Switzerland) · 2023Article
- Non-invasive detection of anemia using lip mucosa images transfer learning convolutional neural networks.Frontiers in big data · 2023Article
- RETRACTED ARTICLE: Smart healthcare system using integrated and lightweight ECC with private blockchain for multimedia medical data processing.Multimedia tools and applications · 2023Article
- Cloud-based COVID-19 disease prediction system from X-Ray images using convolutional neural network on smartphone.Multimedia tools and applications · 2022Article
- Machine learning generalizability across healthcare settings: insights from multi-site COVID-19 screening.NPJ digital medicine · 2022Article
- Outbreak COVID-19 in Medical Image Processing Using Deep Learning: A State-of-the-Art Review.Archives of computational methods in engineering : state of the art reviews · 2022Review
- An Efficient Stacked Deep Transfer Learning Model for Automated Diagnosis of Lyme Disease.Computational intelligence and neuroscience · 2022Article
- Artificial Intelligence of Things- (AIoT-) Based Patient Activity Tracking System for Remote Patient Monitoring.Journal of healthcare engineering · 2022Article
- Performance Evaluation of the Deep Learning Based Convolutional Neural Network Approach for the Recognition of Chest X-Ray Images.Frontiers in oncology · 2022Article
- A Deep Learning and Handcrafted Based Computationally Intelligent Technique for Effective COVID-19 Detection from X-ray/CT-scan Imaging.Journal of grid computing · 2022Article
- A Survey of Soft Computing Approaches in Biomedical Imaging.Journal of healthcare engineering · 2021Review
- Secure Patient Authentication Framework in the Healthcare System Using Wireless Medical Sensor Networks.Journal of healthcare engineering · 2021Article
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4 authors.
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Abstract
Around the world, more than 250 countries are affected by the COVID-19 pandemic, which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This outbreak can be controlled only by the diagnosis of the COVID-19 infection in early stages. It is found that the radiographic images are ideal for the fastest diagnosis of COVID-19 infection. This paper proposes an ensemble model which detects the COVID-19 infection in the early stage with the use of chest X-ray images. The transfer learning enables to reuse the pretrained models. The ensemble learning integrates various transfer learning models, i.e., EfficientNet, GoogLeNet, and XceptionNet, to design the proposed model. These models can categorize patients as COVID-19 (+), pneumonia (+), tuberculosis (+), or healthy. The proposed model enhances the classifier's generalization ability for both binary and multiclass COVID-19 datasets. Two popular datasets are used to evaluate the performance of the proposed ensemble model. The comparative analysis validates that the proposed model outperforms the state-of-art models in terms of various performance metrics.
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