Cov-Net: A computer-aided diagnosis method for recognizing COVID-19 from chest X-ray images via machine vision

Li, Han; Zeng, Nianyin; Wu, Peishu; Clawson, Kathy

doi:10.1016/j.eswa.2022.118029

Cited by 92 publications

(20 citation statements)

References 47 publications

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“…In addition, their dataset only includes images from the viral pneumonia class in the pneumonia category and does not include any images in the tuberculosis class, which leads to a higher classification accuracy. Li et al [ 14 ] have identified a performance similar to that of the model we proposed. The success of the Cov-Net model in classification is noteworthy.…”

Section: Resultssupporting

confidence: 68%

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Detection of Lung Opacity and Treatment Planning with Three-Channel Fusion CNN Model

Turk

Kökver

2023

Arab J Sci Eng

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Lung opacities are extremely important for physicians to monitor and can have irreversible consequences for patients if misdiagnosed or confused with other findings. Therefore, long-term monitoring of the regions of lung opacity is recommended by physicians. Tracking the regional dimensions of images and classifying differences from other lung cases can provide significant ease to physicians. Deep learning methods can be easily used for the detection, classification, and segmentation of lung opacity. In this study, a three-channel fusion CNN model is applied to effectively detect lung opacity on a balanced dataset compiled from public datasets. The MobileNetV2 architecture is used in the first channel, the InceptionV3 model in the second channel, and the VGG19 architecture in the third channel. The ResNet architecture is used for feature transfer from the previous layer to the current layer. In addition to being easy to implement, the proposed approach can also provide significant cost and time advantages to physicians. Our accuracy values for two, three, four, and five classes on the newly compiled dataset for lung opacity classifications are found to be 92.52%, 92.44%, 87.12%, and 91.71%, respectively.

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Section: Resultssupporting

confidence: 68%

“…Li et al [ 14 ] proposed the Cov-Net model for the detection of four-class (lung opacity, COVID-19, viral pneumonia, and normal) radiological images. A modified residual network with asymmetric convolution and embedded attention mechanism was used as a backbone of the feature extractor for accurate detection of classes.…”

Section: Literature Reviewmentioning

confidence: 99%

Detection of Lung Opacity and Treatment Planning with Three-Channel Fusion CNN Model

Turk

Kökver

2023

Arab J Sci Eng

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“…Deep learning is widely used in the biomedical field (Zeng et al, 2021;Li et al, 2022b;Wu et al, 2022), and subsequent applications of deep learning can be considered in this paper.…”

Section: Discussionmentioning

confidence: 99%

TV-NARX and Coiflets WPT based time-frequency Granger causality with application to corticomuscular coupling in hand-grasping

Zhu

Shi

et al. 2022

Front. Neurosci.

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The study of the synchronous characteristics and functional connections between the functional cortex and muscles of hand-grasping movements is important in basic research, clinical disease diagnosis and rehabilitation evaluation. The electroencephalogram (EEG) and electromyographic signal (EMG) signals of 15 healthy participants were used to analyze the corticomuscular coupling under grasping movements by holding three different objects, namely, card, ball, and cup by using the time-frequency Granger causality method based on time-varying nonlinear autoregressive with exogenous input (TV-NARX) model and Coiflets wavelet packet transform. The results show that there is a bidirectional coupling between cortex and muscles under grasping movement, and it is mainly reflected in the beta and gamma frequency bands, in which there is a statistically significant difference (p < 0.05) among the different grasping actions during the movement execution period in the beta frequency band, and a statistically significant difference (p < 0.1) among the different grasping actions during the movement preparation period in the gamma frequency band. The results show that the proposed method can effectively characterize the EEG-EMG synchronization features and functional connections in different frequency bands during the movement preparation and execution phases in the time-frequency domain, and reveal the neural control mechanism of sensorimotor system to control the hand-grasping function achievement by regulating the intensity of neuronal synchronization oscillations.

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“…The study conducted by Li et al [ 106 ] applied the Cov-Net model for the three-way and four-way classification of COVID-19, non-COVID-19 viral pneumonia, and lung opacities acquired from two public accessible datasets. The first dataset contains three categories, named as D1, while the second dataset contained for classes named as D2.…”

Section: Diagnostic Imagingmentioning

confidence: 99%

Current Technologies for Detection of COVID-19: Biosensors, Artificial Intelligence and Internet of Medical Things (IoMT): Review

Irkham

Ibrahim

Nwekwo

et al. 2022

Sensors

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Despite the fact that COVID-19 is no longer a global pandemic due to development and integration of different technologies for the diagnosis and treatment of the disease, technological advancement in the field of molecular biology, electronics, computer science, artificial intelligence, Internet of Things, nanotechnology, etc. has led to the development of molecular approaches and computer aided diagnosis for the detection of COVID-19. This study provides a holistic approach on COVID-19 detection based on (1) molecular diagnosis which includes RT-PCR, antigen–antibody, and CRISPR-based biosensors and (2) computer aided detection based on AI-driven models which include deep learning and transfer learning approach. The review also provide comparison between these two emerging technologies and open research issues for the development of smart-IoMT-enabled platforms for the detection of COVID-19.

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Cov-Net: A computer-aided diagnosis method for recognizing COVID-19 from chest X-ray images via machine vision

Cited by 92 publications

References 47 publications

Detection of Lung Opacity and Treatment Planning with Three-Channel Fusion CNN Model

Detection of Lung Opacity and Treatment Planning with Three-Channel Fusion CNN Model

TV-NARX and Coiflets WPT based time-frequency Granger causality with application to corticomuscular coupling in hand-grasping

Current Technologies for Detection of COVID-19: Biosensors, Artificial Intelligence and Internet of Medical Things (IoMT): Review

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