ArticleScientific reports2021
Fusion of multi-scale bag of deep visual words features of chest X-ray images to detect COVID-19 infection.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- AirSeg: Learnable Interconnected Attention Framework for Robust Airway Segmentation.Journal of imaging informatics in medicine · 2026Article
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- Detection of Pneumonia from Chest X-ray Images Utilizing MobileNet Model.Healthcare (Basel, Switzerland) · 2023Article
- Automated prediction of COVID-19 severity upon admission by chest X-ray images and clinical metadata aiming at accuracy and explainability.Scientific reports · 2023Article
- Artificial intelligence-assisted multistrategy image enhancement of chest X-rays for COVID-19 classification.Quantitative imaging in medicine and surgery · 2023Article
- Deep SVDD and Transfer Learning for COVID-19 Diagnosis Using CT Images.Computational intelligence and neuroscience · 2023Article
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- BND-VGG-19: A deep learning algorithm for COVID-19 identification utilizing X-ray images.Knowledge-based systems · 2022Article
- Explanatory classification of CXR images into COVID-19, Pneumonia and Tuberculosis using deep learning and XAI.Computers in biology and medicine · 2022Article
- Monkeypox Virus Detection Using Pre-trained Deep Learning-based Approaches.Journal of medical systems · 2022Article
- A Light Deep Learning Algorithm for CT Diagnosis of COVID-19 Pneumonia.Diagnostics (Basel, Switzerland) · 2022Article
- MixPatch: A New Method for Training Histopathology Image Classifiers.Diagnostics (Basel, Switzerland) · 2022Article
- Secure Image Encryption Using Chaotic, Hybrid Chaotic and Block Cipher Approach.Journal of imaging · 2022Article
- Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chest X-ray (CXR) images have been one of the important diagnosis tools used in the COVID-19 disease diagnosis. Deep learning (DL)-based methods have been used heavily to analyze these images. Compared to other DL-based methods, the bag of deep visual words-based method (BoDVW) proposed recently is shown to be a prominent representation of CXR images for their better discriminability. However, single-scale BoDVW features are insufficient to capture the detailed semantic information of the infected regions in the lungs as the resolution of such images varies in real application. In this paper, we propose a new multi-scale bag of deep visual words (MBoDVW) features, which exploits three different scales of the 4th pooling layer's output feature map achieved from VGG-16 model. For MBoDVW-based features, we perform the Convolution with Max pooling operation over the 4th pooling layer using three different kernels: [Formula: see text], [Formula: see text], and [Formula: see text]. We evaluate our proposed features with the Support Vector Machine (SVM) classification algorithm on four CXR public datasets (CD1, CD2, CD3, and CD4) with over 5000 CXR images. Experimental results show that our method produces stable and prominent classification accuracy (84.37%, 88.88%, 90.29%, and 83.65% on CD1, CD2, CD3, and CD4, respectively).
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