ArticleExpert systems with applications2022
Cov-Net: A computer-aided diagnosis method for recognizing COVID-19 from chest X-ray images via machine vision.
Article in Expert systems with applications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
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- Generative deep learning for the development of a type 1 diabetes simulator.Communications medicine · 2024Article
- COVID-19 detection and analysis from lung CT images using novel channel boosted CNNs.Expert systems with applications · 2023Article
- MDF-Net for abnormality detection by fusing X-rays with clinical data.Scientific reports · 2023Article
- Deep residual-dense network based on bidirectional recurrent neural network for atrial fibrillation detection.Scientific reports · 2023Article
- Generative adversarial networks based skin lesion segmentation.Scientific reports · 2023Article
- Neural Networks for the Detection of COVID-19 and Other Diseases: Prospects and Challenges.Bioengineering (Basel, Switzerland) · 2023Article
- Optimal Feature Analysis for Identification Based on Intracranial Brain Signals with Machine Learning Algorithms.Bioengineering (Basel, Switzerland) · 2023Article
- Prehospital stroke-scale machine-learning model predicts the need for surgical intervention.Scientific reports · 2023Article
- Progressive attention integration-based multi-scale efficient network for medical imaging analysis with application to COVID-19 diagnosis.Computers in biology and medicine · 2023Article
- Optimizing the input feature sets and machine learning algorithms for reliable and accurate estimation of continuous, cuffless blood pressure.Scientific reports · 2023Article
- COVID-ConvNet: A Convolutional Neural Network Classifier for Diagnosing COVID-19 Infection.Diagnostics (Basel, Switzerland) · 2023Article
- Classification of chest X-ray images by incorporation of medical domain knowledge into operation branch networks.BMC medical imaging · 2023Article
- A Real Time Method for Distinguishing COVID-19 Utilizing 2D-CNN and Transfer Learning.Sensors (Basel, Switzerland) · 2023Article
- Current Technologies for Detection of COVID-19: Biosensors, Artificial Intelligence and Internet of Medical Things (IoMT): Review.Sensors (Basel, Switzerland) · 2022Review
- Brain white matter hyperintensity lesion characterization in 3D TFrontiers in neuroscience · 2022Article
- TV-NARX and Coiflets WPT based time-frequency Granger causality with application to corticomuscular coupling in hand-grasping.Frontiers in neuroscience · 2022Article
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4 authors.
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Abstract
In the context of global pandemic Coronavirus disease 2019 (COVID-19) that threatens life of all human beings, it is of vital importance to achieve early detection of COVID-19 among symptomatic patients. In this paper, a computer aided diagnosis (CAD) model Cov-Net is proposed for accurate recognition of COVID-19 from chest X-ray images via machine vision techniques, which mainly concentrates on powerful and robust feature learning ability. In particular, a modified residual network with asymmetric convolution and attention mechanism embedded is selected as the backbone of feature extractor, after which skip-connected dilated convolution with varying dilation rates is applied to achieve sufficient feature fusion among high-level semantic and low-level detailed information. Experimental results on two public COVID-19 radiography databases have demonstrated the practicality of proposed Cov-Net in accurate COVID-19 recognition with accuracy of 0.9966 and 0.9901, respectively. Furthermore, within same experimental conditions, proposed Cov-Net outperforms other six state-of-the-art computer vision algorithms, which validates the superiority and competitiveness of Cov-Net in building highly discriminative features from the perspective of methodology. Hence, it is deemed that proposed Cov-Net has a good generalization ability so that it can be applied to other CAD scenarios. Consequently, one can conclude that this work has both practical value in providing reliable reference to the radiologist and theoretical significance in developing methods to build robust features with strong presentation ability.
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