ArticleBiomedical signal processing and control2021
Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images.
Article in Biomedical signal processing and control, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 3 of them syntheses that pooled it.
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Who cites it
60 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Automated COVID-19 diagnosis and prognosis with medical imaging and who is publishing: a systematic review.Physical and engineering sciences in medicine · 2022Pooled it
- Application of artificial intelligence in diagnosing COVID-19 disease symptoms on chest X-rays: A systematic review.International journal of medical sciences · 2022Pooled it
- Role of Artificial Intelligence in COVID-19 Detection.Sensors (Basel, Switzerland) · 2021Pooled it
- Review
- Deep learning approaches for classification tasks in medical X-ray, MRI, and ultrasound images: a scoping review.BMC medical imaging · 2025Article
- Optical sorting: past, present and future.Light, science & applications · 2025Review
- RETRACTED: A novel spectral transformation technique based on special functions for improved chest X-ray image classification.PloS one · 2025Article
- A Pathological Diagnosis Method for Fever of Unknown Origin Based on Multipath Hierarchical Classification: Model Design and Validation.JMIR formative research · 2024Article
- Integrating hybrid transfer learning with attention-enhanced deep learning models to improve breast cancer diagnosis.PeerJ. Computer science · 2024Article
- Article
- Article
- Automatic diagnosis of COVID-19 from CT images using CycleGAN and transfer learning.Applied soft computing · 2023Article
- A comprehensive review of COVID-19 detection with machine learning and deep learning techniques.Health and technology · 2023Article
- CTMLP: Can MLPs replace CNNs or transformers for COVID-19 diagnosis?Computers in biology and medicine · 2023Article
- A survey of machine learning-based methods for COVID-19 medical image analysis.Medical & biological engineering & computing · 2023Review
- A comprehensive review of analyzing the chest X-ray images to detect COVID-19 infections using deep learning techniques.Soft computing · 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
- Automatic segmentation of thyroid with the assistance of the devised boundary improvement based on multicomponent small dataset.Applied intelligence (Dordrecht, Netherlands) · 2023Article
- COVID-19 radiograph prognosis using a deep CResNeXt network.Multimedia tools and applications · 2023Article
- RADIC:A tool for diagnosing COVID-19 from chest CT and X-ray scans using deep learning and quad-radiomics.Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society · 2023Article
Corrections and comments
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Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The coronavirus (COVID-19) is currently the most common contagious disease which is prevalent all over the world. The main challenge of this disease is the primary diagnosis to prevent secondary infections and its spread from one person to another. Therefore, it is essential to use an automatic diagnosis system along with clinical procedures for the rapid diagnosis of COVID-19 to prevent its spread. Artificial intelligence techniques using computed tomography (CT) images of the lungs and chest radiography have the potential to obtain high diagnostic performance for Covid-19 diagnosis. In this study, a fusion of convolutional neural network (CNN), support vector machine (SVM), and Sobel filter is proposed to detect COVID-19 using X-ray images. A new X-ray image dataset was collected and subjected to high pass filter using a Sobel filter to obtain the edges of the images. Then these images are fed to CNN deep learning model followed by SVM classifier with ten-fold cross validation strategy. This method is designed so that it can learn with not many data. Our results show that the proposed CNN-SVM with Sobel filter (CNN-SVM + Sobel) achieved the highest classification accuracy, sensitivity and specificity of 99.02%, 100% and 95.23%, respectively in automated detection of COVID-19. It showed that using Sobel filter can improve the performance of CNN. Unlike most of the other researches, this method does not use a pre-trained network. We have also validated our developed model using
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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.