COVID-19 detection method based on SVRNet and SVDNet in lung x-rays

Rao, K. D. M.; Xie, Kai; Hu, Ziqi; Guo, Xiaolong; Wen, Chang; He, Jing

doi:10.1117/1.jmi.8.s1.017504

Cited by 3 publications

(1 citation statement)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Their method achieved a sensitivity, specificity and accuracy of 97%, 98% and 98%, respectively. Rao et al [ 58 ] stated that separable SVRNet and separable SVDNet models greatly reduced the number of parameters while improving the accuracy and increasing the operating speed.…”

Section: Role Of 18-fluorodeoxyglucose Positron Emission Tomographymentioning

confidence: 99%

Augmentation of literature review of COVID-19 radiology

Merchant¹,

Nadkarni²,

Shaikh³

2022

World J Radiol

View full text Add to dashboard Cite

We suggest an augmentation of the excellent comprehensive review article titled “Comprehensive literature review on the radiographic findings, imaging modalities, and the role of radiology in the coronavirus disease 2019 (COVID-19) pandemic” under the following categories: (1) “Inclusion of additional radiological features, related to pulmonary infarcts and to COVID-19 pneumonia”; (2) “Amplified discussion of cardiovascular COVID-19 manifestations and the role of cardiac magnetic resonance imaging in monitoring and prognosis”; (3) “Imaging findings related to fluorodeoxyglucose positron emission tomography, optical, thermal and other imaging modalities/devices, including ‘intelligent edge’ and other remote monitoring devices”; (4) “Artificial intelligence in COVID-19 imaging”; (5) “Additional annotations to the radiological images in the manuscript to illustrate the additional signs discussed”; and (6) “A minor correction to a passage on pulmonary destruction”.

show abstract

Section: Role Of 18-fluorodeoxyglucose Positron Emission Tomographymentioning

confidence: 99%

Augmentation of literature review of COVID-19 radiology

Merchant¹,

Nadkarni²,

Shaikh³

2022

World J Radiol

View full text Add to dashboard Cite

show abstract

Detection of Covid-19 early symptoms with higher accuracy using novel chest X-rays images using VGG-16 network comparing residual neural network

Arshathraj,

Rajendran

2023

INTERNATIONAL CONFERENCE ON SCIENCE, ENGINEERING, AND TECHNOLOGY 2022: Conference Proceedings

View full text Add to dashboard Cite

Convergence Track Based Adaptive Differential Evolution Algorithm (CTbADE)

Abbas¹,

Malik²,

Saudagar³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

One of the challenging problems with evolutionary computing algorithms is to maintain the balance between exploration and exploitation capability in order to search global optima. A novel convergence track based adaptive differential evolution (CTbADE) algorithm is presented in this research paper. The crossover rate and mutation probability parameters in a differential evolution algorithm have a significant role in searching global optima. A more diverse population improves the global searching capability and helps to escape from the local optima problem. Tracking the convergence path over time helps enhance the searching speed of a differential evolution algorithm for varying problems. An adaptive powerful parameter-controlled sequences utilized learning period-based memory and following convergence track over time are introduced in this paper. The proposed algorithm will be helpful in maintaining the equilibrium between an algorithm's exploration and exploitation capability. A comprehensive test suite of standard benchmark problems with different natures, i.e., unimodal/multimodal and separable/non-separable, was used to test the convergence power of the proposed CTbADE algorithm. Experimental results show the significant performance of the CTbADE algorithm in terms of average fitness, solution quality, and convergence speed when compared with standard differential evolution algorithms and a few other commonly used state-of-the-art algorithms, such as jDE, CoDE, and EPSDE algorithms. This algorithm will prove to be a significant addition to the literature in order to solve real time problems and to optimize computational models with a high number of parameters to adjust during the problem-solving process.

show abstract

COVID-19 detection method based on SVRNet and SVDNet in lung x-rays

Cited by 3 publications

References 23 publications

Augmentation of literature review of COVID-19 radiology

Augmentation of literature review of COVID-19 radiology

Detection of Covid-19 early symptoms with higher accuracy using novel chest X-rays images using VGG-16 network comparing residual neural network

Convergence Track Based Adaptive Differential Evolution Algorithm (CTbADE)

Contact Info

Product

Resources

About