Web-Based Diagnosis of Corona Disease using a Deep Learning Model

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HEIDARI ALI; Erfanian Hamid Reza

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Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2022 | Issue:8

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Title

Web-Based Diagnosis of Corona Disease using a Deep Learning Model

Author(s)

HEIDARI ALI | Erfanian Hamid Reza

Keywords

Covid-19

Deep learning

Alexent model

Medical imaging processing

Pneumonia

Abstract

Covid-19 virus, which is a mutated species of the coronavirus family, was announced as a pandemic by the World Health Organization in March 2020. The death toll from the virus at the beginning of 2022 is close to six million. So far, several methods have been introduced to diagnose the virus, which are mostly difficult to access or require a high cost, and of course, some of these methods are time consuming. Therefore, providing a smart web-based service that is the main goal of this research is very important, especially since this service uses the most up-to-date Deep learning techniques to diagnose a person with Covid 19 disease. In this paper, the Deep learning approach is used and the Alexent model is used in this process. The data used in this study include 8840 chest images, half of which are related to people without Covid 19 virus involvement and the other half are related to patients with Covid 19 virus. The model proved to be very reliable with 99. 26% accuracy in diagnosis and 95% sensitivity and 99. 7% specificity.

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APA: Copy

HEIDARI, ALI, & Erfanian, Hamid Reza. (2022). Web-Based Diagnosis of Corona Disease using a Deep Learning Model. INTERNATIONAL CONFERENCE ON WEB RESEARCH. SID. https://sid.ir/paper/949663/en

Vancouver: Copy

HEIDARI ALI, Erfanian Hamid Reza. Web-Based Diagnosis of Corona Disease using a Deep Learning Model. 2022. Available from: https://sid.ir/paper/949663/en

IEEE: Copy

ALI HEIDARI, and Hamid Reza Erfanian, “Web-Based Diagnosis of Corona Disease using a Deep Learning Model,” presented at the INTERNATIONAL CONFERENCE ON WEB RESEARCH. 2022, [Online]. Available: https://sid.ir/paper/949663/en

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