A New Imaging Method for Forward-looking Linear-array SAR based on Fractional Fourier Transform

Yuan, Yue; Chen, Si; Zhao, Huichang; Wu, Wei Biao; Fleetwood, D.M.; Zhang, Enxia

doi:10.1109/radar.2019.8835678

Cited by 2 publications

(2 citation statements)

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“…The image analysis relied on a ResNet50 model for its features. The achieved result demonstrated an AUC of 73.2%, an Accuracy of 70 %, a Sensitivity of 73%, and a Specificity of 61.5% [42].…”

Section: Literature Reviewmentioning

confidence: 91%

“…In unusual cases, individuals may have slight, self-restraining infections with adversative properties like influenza. There are several symptoms of respiratory concerns, including fever, coughing, and having trouble breathing, as well as exhaustion, a sore throat, and a weak immune system [5,42,24,41,31]. The use of CT scans and X-rays is without a doubt one of the most important methods for the detection of COVID-19.…”

Section: Introductionmentioning

confidence: 99%

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COVID-19 Diagnosis by Extracting New Features from Lung CT Images Using Fractional Fourier Transform

Rashidi,

Nokhostin

2023

Preprint

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Covid-19 is a lung disease caused by a Coronavirus family virus. Due to its extraordinary prevalence and death rates, it has spread quickly to every country in the world. Thus, achieving peaks and outlines and curing different types of relapses is extremely important. Given the worldwide prevalence of Coronavirus and the participation of physicians in all countries, Information has been gathered regarding the properties of the virus, its diverse types, and the means of analyzing it. Numerous approaches have been used to identify this evolving virus. It is generally considered the most accurate and acceptable method of examining the patient's lungs and chest through a CT scan. As part of the feature extraction process, a method known as fractional Fourier transform (FrFT) has been applied as one of the time-frequency domain transformations. The proposed method was applied to a database consisting of 2481 CT images. Following the transformation of all images into equal sizes and the removal of non-lung areas, multiple combination windows are used to reduce the number of features extracted from the images. In this paper, the results obtained for KNN and SVM classification have been obtained with accuracy values of 99.84% and 99.90%, respectively.

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Section: Literature Reviewmentioning

confidence: 91%