Diagnosis of COVID-19 Using a Deep Learning Model in Various Radiology Domains
نویسندگان
چکیده
Many countries are severely affected by COVID-19, and various casualties have been reported. Most implemented full partial lockdowns to control COVID-19. Paramedical employee infections always a threatening discovery. Front-line paramedical employees might initially be at risk when observing treating patients, who can contaminate them through respiratory secretions. If proper preventive measures absent, front-line workers will in danger of contamination become unintentional carriers patients admitted the hospital for other illnesses treatments. Moreover, every country has limited testing capacity; therefore, system is required which helps doctor directly check analyze patients’ blood structure. This study proposes generalized adaptive deep learning model that easily detect COVID-19 different radiology domains. In this work, we designed using convolutional neural network order from X-ray, Computed Tomography (CT), Magnetic Resonance Imaging (MRI) images. The proposed 27 layers (input, convolutional, max-pooling, dropout, flatten, dense, output layers), tested validated on domains such as CT, MRI. For experiments, utilized 70% dataset training 30% against each dataset. weighted average accuracies 94%, 85%, 86% MRI, respectively. experiments show significance state-of-the-art works.
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ژورنال
عنوان ژورنال: Complexity
سال: 2021
ISSN: ['1099-0526', '1076-2787']
DOI: https://doi.org/10.1155/2021/1296755