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A Framework Automation of COVID-19 Classification through Chest X-rays using Deep Learning over Cloud
NeuroQuantology ; 20(11):4252-4263, 2022.
Article in English | EMBASE | ID: covidwho-2067343
ABSTRACT
There is a huge the spread of Covid-19 pandemic (Corona) in large areas of the country, including modern and rural areas, and due to the scarcity of medical tools and supplies, especially in rural areas. Therefore, artificial intelligence researchers are using technologies to help detect disease early by using chest X-rays to classify whether or not the disease is present. Note that doctors have agreed in more than one scientific article that the initial examination to detect this disease is carried out through chest x-rays, the devices of which are available in most places.Because the Internet is available in most rural areas and in order to reduce the spread of this pandemic, in this paper we built a project by deep transfer learning using an application in Keras called "InceptionV3" on cloud, this model trained and tested 10 thousand images of people with the disease and others where the data distribution was equal to avoid From imbalanced data, and this model will be used across the cloud by web framework so that we can get proactive decisions and avoid spread. This model has been applied in the Department of Respiratory Medicine at Dr. ShankarraoChavan Government Hospital, Nanded, under the supervision of a medical staff headed by Dr. V. R. Kapse, associate professor and head of the department of pulmonary, we have obtained results after training and evaluating the model are training accuracy 97.6%, testing accuracy 97.5%, precision 97.8%, sensitivity 100% and specificity 99.9%. Copyright © 2022, Anka Publishers. All rights reserved.
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Full text: Available Collection: Databases of international organizations Database: EMBASE Language: English Journal: NeuroQuantology Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: EMBASE Language: English Journal: NeuroQuantology Year: 2022 Document Type: Article