A Deep Convolution Network-Based Pneumonia Identification from Thoracic X-Ray Imagery Scans

Upreti, Kamal; Mishra, Prateet; Maheshwari, Manish; Vats, Prashant; Dhankar, Aisha; Batra, Reenu; Yadav, Saneh Lata; Chawla, Tanvi; Bhardwaj, Jayant

doi:10.1007/978-981-19-7660-5_64

Cited by 11 publications

(1 citation statement)

References 15 publications

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“…The LUS method may be useful in determining the presence or absence of an active infection, and it certainly has the advantage of being less expensive [24]. Chest ultrasounds have limited use in medicine, being limited to detecting abnormalities in the lungs' periphery [7].…”

Section: Sound Wave Range 2 To 18 Mhzmentioning

confidence: 99%

Comparative Analysis of COVID - 19 Image Processing With X-Ray And CT Scan

Arora,

Parashar,

Upreti

et al. 2023

APFCB

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the SARS-COV2 (COVID-19) virus continues to spread over the world in the form of various mutant strains that are responsible for epidemic conditions. As a result, the clinical prognosis cannot be relied upon. Chest x-rays, computed tomography, and ultrasound imaging models supplement the analytical methods (for example, RT-PCR) to a certain degree, despite the fact that different clinical diagnostic approaches have been developed and used up to this point. The purpose of this work is to carry out the diagnosis of COVID-19 individuals utilizing the image padding technique, as well as to develop a Deep Covix-Net model for the identification of COVID-19, Pneumonia, and healthy individuals utilizing X-ray and CT scan images. Both of these objectives will be accomplished through this work. Using ultrasound pictures, create a model called Ultra Covix-Net for early diagnosis of COVID-19, pneumonia, and healthy persons; then, using CT scan images, estimate the severity of the COVID-19 infection. In order to complete the categorization process, machine learning and deep learning strategies are used.

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Section: Sound Wave Range 2 To 18 Mhzmentioning

confidence: 99%