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Utilization of artificial intelligence in medical image analysis for COVID19 patients detection - 2021

Research Area:  Machine Learning

Abstract:

In the era of medical technology, automatic scan detection can be considered a charming tool in medical diagnosis, especially with rapidly spreading diseases. In light of the prevalence of the current Coronavirus disease (COVID-19), which is characterized as highly contagious and very complicated, it is urgent and necessary to find a quick way that can be practically implemented for diagnosing COVID-19. The danger of the virus lies in the fact that patients can spread the disease without showing any symptoms. Moreover, several vaccines have been produced and vaccinated in large numbers but, the outbreak does not stop. Therefore, it is urgent and necessary to find a quick way that can be practically implemented for diagnosing COVID-19 cases. One of the most important ways to combat this disease is the early detection of the virus by using technology to identify and isolate patients. The combined results of recent researches showed that both CT and CXR scan correctly diagnosed COVID-19 in 87% and 80% of infected people. From these perspectives, this research paper aims to employ a deep learning model using the convolutional neural network (CNN) to detect and diagnose COVID-19 from both CT and CXR scans. The CNN is being used for features extraction and then detect COVID-19 cases in a little bit of time. Dataset collections of CT and CXR scans are being applied for examining the proposed CNN-based COVID-19 detection model. The results show that the proposed CNN-based COVID-19 detection model can achieve an accuracy of 99%, effectively speeding up the diagnosis and treatment of COVID-19 patients from CT and CXR scans.

Author(s) Name:  Mohammed Baz, Hatem Zaini, Hala S. El-sayed, Matokah AbuAlNaja, Heba M. El-Hoseny, Osama S. Faragallah

Journal name:  Intelligent Automation & Soft Computing

Conferrence name:  

Publisher name:  TECH SCIENCE PRESS

DOI:  10.32604/iasc.2021.018265

Volume Information:  Vol.30, No.1, 2021, pp.97-111