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Neural Architecture Search Network for the Diagnosis of COVID From the Radiographic Images
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Author(s): Sasikaladevi N. (School of Computing, SASTRA University (Deemed), India)and Revathi A. (School of EEE, SASTRA University (Deemed), India)
Copyright: 2022
Pages: 14
Source title:
Applications of Computational Science in Artificial Intelligence
Source Author(s)/Editor(s): Anand Nayyar (Duy Tan University, Da Nang, Vietnam), Sandeep Kumar (CHRIST University (Deemed), Bangalore, India)and Akshat Agrawal (Amity University, Guragon, India)
DOI: 10.4018/978-1-7998-9012-6.ch004
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Abstract
The outbreak of human-to-human transmissible COVID-19 has caused approximately 64,000 deaths around the world and keeps continuously increasing in an exponential order that has provoked global alarm. To control the spread of the disease, screening large numbers of suspected cases for appropriate quarantine and treatment measures is of higher priority. Since clinical laboratory testing with precise accuracy for huge samples in the infected region remains a great challenge that demands complementary diagnostic methods to combat the disease. In this work, the authors have identified a new AI-based deep learning framework named CORONATE based on neural architecture space search network (NASNET) as a competent choice that can extract graphical features from radiography images referred from the public dataset of x-ray images. This observation endorses that CORONATE model can administer a faster clinical diagnosis well ahead of pathogenic tests with higher accuracy and can empower the medical team to ensure a good control on the outbreak by saving critical diagnosis time.
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