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A lightweight capsule network architecture for detection of COVID ‐19 from lung CT scans
Author(s) -
Tiwari Shamik,
Jain Anurag
Publication year - 2022
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.22706
Subject(s) - computer science , covid-19 , convolutional neural network , mobile device , deep learning , computed tomography , android (operating system) , artificial intelligence , real time computing , infectious disease (medical specialty) , medicine , pathology , radiology , disease , operating system
COVID‐19, a novel coronavirus, has spread quickly and produced a worldwide respiratory ailment outbreak. There is a need for large‐scale screening to prevent the spreading of the disease. When compared with the reverse transcription polymerase chain reaction (RT‐PCR) test, computed tomography (CT) is far more consistent, concrete, and precise in detecting COVID‐19 patients through clinical diagnosis. An architecture based on deep learning has been proposed by integrating a capsule network with different variants of convolution neural networks. DenseNet, ResNet, VGGNet, and MobileNet are utilized with CapsNet to detect COVID‐19 cases using lung computed tomography scans. It has found that all the four models are providing adequate accuracy, among which the VGGCapsNet, DenseCapsNet, and MobileCapsNet models have gained the highest accuracy of 99%. An Android‐based app can be deployed using MobileCapsNet model to detect COVID‐19 as it is a lightweight model and best suited for handheld devices like a mobile.