ArticleMultimedia systems2023
Medical image-based detection of COVID-19 using Deep Convolution Neural Networks.
Article in Multimedia systems, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.
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Who cites it
29 citing papers in PubMed, 2 syntheses or guidelines pooled it, 224 citations in OpenAlex.
- Gauging the Impact of Artificial Intelligence and Mathematical Modeling in Response to the COVID-19 Pandemic: A Systematic Review.BioMed research international · 2022Pooled it
- Role of Artificial Intelligence in COVID-19 Detection.Sensors (Basel, Switzerland) · 2021Pooled it
- Uncertainty Quantification in Medical Image Segmentation: A Comprehensive Survey.Journal of imaging · 2026Review
- An image recognition agorithm for fine-grained high-frequency workpieces based on a multi-branch network architecture.Scientific reports · 2026Article
- Systematic investigation of pre-processing and feature extraction techniques in medical image analysis.Discover artificial intelligence · 2026Article
- Diffusion-based skin disease data augmentation with fine-grained detail preservation and interpolation for data diversity.PloS one · 2025Article
- Deep Learning for Pneumonia Detection in Chest X-ray Images: A Comprehensive Survey.Journal of imaging · 2024Review
- A variational autoencoder trained with priors from canonical pathways increases the interpretability of transcriptome data.PLoS computational biology · 2024Article
- Editorial: Advancements of deep learning in medical imaging for neurodegenerative diseases.Frontiers in neuroscience · 2024Article
- Deep Learning-Based Knee MRI Classification for Common Peroneal Nerve Palsy with Foot Drop.Biomedicines · 2023Article
- COVID-19 Detection via Ultra-Low-Dose X-ray Images Enabled by Deep Learning.Bioengineering (Basel, Switzerland) · 2023Article
- Parasitic egg recognition using convolution and attention network.Scientific reports · 2023Article
- Optimized Xception Learning Model and XgBoost Classifier for Detection of Multiclass Chest Disease from X-ray Images.Diagnostics (Basel, Switzerland) · 2023Article
- A Novel Deep Learning-Based Classification Framework for COVID-19 Assisted with Weighted Average Ensemble Modeling.Diagnostics (Basel, Switzerland) · 2023Article
- Deep Neural Network Augments Performance of Junior Residents in Diagnosing COVID-19 Pneumonia on Chest Radiographs.Diagnostics (Basel, Switzerland) · 2023Article
- Robust Classification and Detection of Big Medical Data Using Advanced ParallelLife (Basel, Switzerland) · 2023Article
- IoT-Based Medical Image Monitoring System Using HL7 in a Hospital Database.Healthcare (Basel, Switzerland) · 2023Article
- AI-Powered Blockchain Technology for Public Health: A Contemporary Review, Open Challenges, and Future Research Directions.Healthcare (Basel, Switzerland) · 2022Review
- Human Fall Detection Using 3D Multi-Stream Convolutional Neural Networks with Fusion.Diagnostics (Basel, Switzerland) · 2022Article
- Deep viewing for the identification of Covid-19 infection status from chest X-Ray image using CNN based architecture.Intelligent systems with applications · 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
5 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The demand for automatic detection of Novel Coronavirus or COVID-19 is increasing across the globe. The exponential rise in cases burdens healthcare facilities, and a vast amount of multimedia healthcare data is being explored to find a solution. This study presents a practical solution to detect COVID-19 from chest X-rays while distinguishing those from normal and impacted by Viral Pneumonia via Deep Convolution Neural Networks (CNN). In this study, three pre-trained CNN models (EfficientNetB0, VGG16, and InceptionV3) are evaluated through transfer learning. The rationale for selecting these specific models is their balance of accuracy and efficiency with fewer parameters suitable for mobile applications. The dataset used for the study is publicly available and compiled from different sources. This study uses deep learning techniques and performance metrics (accuracy, recall, specificity, precision, and F1 scores). The results show that the proposed approach produced a high-quality model, with an overall accuracy of 92.93%, COVID-19, a sensitivity of 94.79%. The work indicates a definite possibility to implement computer vision design to enable effective detection and screening measures.
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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.