ArticleIEEE access : practical innovations, open solutions2021
COVID-19 Detection Based on Image Regrouping and Resnet-SVM Using Chest X-Ray Images.
Article in IEEE access : practical innovations, open solutions, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Role of Artificial Intelligence in COVID-19 Detection.Sensors (Basel, Switzerland) · 2021Pooled it
- Applications of Technological Solutions in Primary Ways of Preventing Transmission of Respiratory Infectious Diseases-A Systematic Literature Review.International journal of environmental research and public health · 2021Pooled it
- Deep Learning Assisted Classification of T1ρ-MR Based Intervertebral Disc Degeneration Phases.Journal of magnetic resonance imaging : JMRI · 2025Article
- Corticosteroid treatment prediction using chest X-ray and clinical data.Computational and structural biotechnology journal · 2024Article
- Study on lung CT image segmentation algorithm based on threshold-gradient combination and improved convex hull method.Scientific reports · 2024Article
- COVID-19 Detection via Ultra-Low-Dose X-ray Images Enabled by Deep Learning.Bioengineering (Basel, Switzerland) · 2023Article
- Detection of Corneal Ulcer Using a Genetic Algorithm-Based Image Selection and Residual Neural Network.Bioengineering (Basel, Switzerland) · 2023Article
- A Novel Deep Learning-Based Classification Framework for COVID-19 Assisted with Weighted Average Ensemble Modeling.Diagnostics (Basel, Switzerland) · 2023Article
- McS-Net: Multi-class Siamese network for severity of COVID-19 infection classification from lung CT scan slices.Applied soft computing · 2022Article
- Recommender System for the Efficient Treatment of COVID-19 Using a Convolutional Neural Network Model and Image Similarity.Diagnostics (Basel, Switzerland) · 2022Article
- Research on multi-modal autonomous diagnosis algorithm of COVID-19 based on whale optimized support vector machine and improved D-S evidence fusion.Computers in biology and medicine · 2022Article
- Chest X-ray analysis empowered with deep learning: A systematic review.Applied soft computing · 2022Article
- BEMD-3DCNN-based method for COVID-19 detection.Computers in biology and medicine · 2022Article
- Bearing Fault Reconstruction Diagnosis Method Based on ResNet-152 with Multi-Scale Stacked Receptive Field.Sensors (Basel, Switzerland) · 2022Article
- Detecting COVID-19 Pneumonia over Fuzzy Image Enhancement on Computed Tomography Images.Computational and mathematical methods in medicine · 2022Article
- COVID-RDNet: A novel coronavirus pneumonia classification model using the mixed dataset by CT and X-rays images.Biocybernetics and biomedical engineeringArticle
- A computer-aided diagnostic framework for coronavirus diagnosis using texture-based radiomics images.Digital healthArticle
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5 authors.
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Abstract
As the COVID-19 spread worldwide, countries around the world are actively taking measures to fight against the epidemic. To prevent the spread of it, a high sensitivity and efficient method for COVID-19 detection is necessary. By analyzing the COVID-19 chest X-ray images, a combination method of image regrouping and ResNet-SVM was proposed in this study. The lung region was segmented from the original chest X-ray images and divided into small pieces, and then the small pieces of lung region were regrouped into a regular image randomly. Furthermore the regrouped images were fed into the deep residual encoder block for feature extraction. Finally the extracted features were as input into support vector machine for recognition. The visual attention was introduced in the novel method, which paid more attention to the features of COVID-19 without the interference of shapes, rib and other related noises. The experimental results showed that the proposed method achieved 93% accuracy without large number of training data, outperformed the existing COVID-19 detection models.
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