ArticleJournal of ambient intelligence and humanized computing2022
COVINet: a convolutional neural network approach for predicting COVID-19 from chest X-ray images.
Article in Journal of ambient intelligence and humanized computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 106 citations in OpenAlex.
- Role of Artificial Intelligence in COVID-19 Detection.Sensors (Basel, Switzerland) · 2021Pooled it
- COVID-19 health data prediction: a critical evaluation of CNN-based approaches.Scientific reports · 2025Article
- Deep Learning for Pneumonia Detection in Chest X-ray Images: A Comprehensive Survey.Journal of imaging · 2024Review
- Nerve Root Compression Analysis to Find Lumbar Spine Stenosis on MRI Using CNN.Diagnostics (Basel, Switzerland) · 2023Article
- Enhancing Cricket Performance Analysis with Human Pose Estimation and Machine Learning.Sensors (Basel, Switzerland) · 2023Article
- Investigating Beta-Variational Convolutional Autoencoders for the Unsupervised Classification of Chest Pneumonia.Diagnostics (Basel, Switzerland) · 2023Article
- Deep Learning Methods for Interpretation of Pulmonary CT and X-ray Images in Patients with COVID-19-Related Lung Involvement: A Systematic Review.Journal of clinical medicine · 2023Review
- Efficient COVID-19 super pixel segmentation algorithm using MCFO-based SLIC.Journal of ambient intelligence and humanized computing · 2023Article
- Hybrid intelligent model for classifying chest X-ray images of COVID-19 patients using genetic algorithm and neutrosophic logic.Soft computing · 2023Article
- Application of Machine Learning and Deep Learning Techniques for COVID-19 Screening Using Radiological Imaging: A Comprehensive Review.SN computer science · 2023Article
- Detection of Covid-19 and other pneumonia cases from CT and X-ray chest images using deep learning based on feature reuse residual block and depthwise dilated convolutions neural network.Applied soft computing · 2023Article
- Sentiment Analysis and Comprehensive Evaluation of Supervised Machine Learning Models Using Twitter Data on Russia-Ukraine War.SN computer science · 2023Article
- DeepCOVNet Model for COVID-19 Detection Using Chest X-Ray Images.Wireless personal communications · 2023Article
- Radiomics-Based Detection of COVID-19 from Chest X-ray Using Interpretable Soft Label-Driven TSK Fuzzy Classifier.Diagnostics (Basel, Switzerland) · 2022Article
- Industry 4.0 technologies and their applications in fighting COVID-19 pandemic using deep learning techniques.Computers in biology and medicine · 2022Article
- A fuzzy-enhanced deep learning approach for early detection of Covid-19 pneumonia from portable chest X-ray images.Neurocomputing · 2022Article
- A deep and handcrafted features-based framework for diagnosis of COVID-19 from chest x-ray images.Concurrency and computation : practice & experience · 2022Article
- A Deep Learning-Based Quantitative Structure-Activity Relationship System Construct Prediction Model of Agonist and Antagonist with High Performance.International journal of molecular sciences · 2022Article
- Novel extreme regression-voting classifier to predict death risk in vaccinated people using VAERS data.PloS one · 2022Article
- Towards edge devices implementation: deep learning model with visualization for COVID-19 prediction from chest X-ray.Advances in computational intelligence · 2022Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID-19 pandemic is widely spreading over the entire world and has established significant community spread. Fostering a prediction system can help prepare the officials to respond properly and quickly. Medical imaging like X-ray and computed tomography (CT) can play an important role in the early prediction of COVID-19 patients that will help the timely treatment of the patients. The x-ray images from COVID-19 patients reveal the pneumonia infections that can be used to identify the patients of COVID-19. This study presents the use of Convolutional Neural Network (CNN) that extracts the features from chest x-ray images for the prediction. Three filters are applied to get the edges from the images that help to get the desired segmented target with the infected area of the x-ray. To cope with the smaller size of the training dataset, Keras' ImageDataGenerator class is used to generate ten thousand augmented images. Classification is performed with two, three, and four classes where the four-class problem has X-ray images from COVID-19, normal people, virus pneumonia, and bacterial pneumonia. Results demonstrate that the proposed CNN model can predict COVID-19 patients with high accuracy. It can help automate screening of the patients for COVID-19 with minimal contact, especially areas where the influx of patients can not be treated by the available medical staff. The performance comparison of the proposed approach with VGG16 and AlexNet shows that classification results for two and four classes are competitive and identical for three-class classification.
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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.