Smart Health Care System for Early Detection of COVID-19 Using X-ray Scans

Document Type

Conference Proceeding

Source of Publication

2022 International Conference on Electrical, Computer and Energy Technologies (ICECET)

Publication Date

7-22-2022

Abstract

The novel Coronavirus spread in the world in December 2019. Millions of people are infected due to this disease. Due to viral illness, daily life routines and the economy are affected in many countries. According to a clinical study, the disease directly attacks the lungs and disturbs the respiratory system. X-ray and CT scans are the main imaging techniques to discover that disease. However, X-ray scans cost is low as comparatively CT scans. In the limited resources, deep learning plays a key role in diagnosing the COVID-19 with the help of X-ray scans. This study proposed a new transfer learning approach based on the convolutional neural network (CNN). We used the four different classes during the experimental process: COVID-19, pneumonia, lung opacity, and viral pneumonia. We also compared our proposed model with other transfer learning-based techniques. Our proposed COVID-TL model attained the best results in terms of classification. The proposed model is a beneficial tool for radiologists to get the early diagnosis results and help the patients in their early stages.

ISBN

978-1-6654-7087-2

Publisher

IEEE

Volume

00

First Page

1

Last Page

4

Disciplines

Computer Sciences

Keywords

COVID-19, Pulmonary diseases, Computed tomography, Transfer learning, Smart healthcare, Lung, Training data

Indexed in Scopus

no

Open Access

no

Link to Full Text

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