COVIDnet: An Efficient Deep Learning Model for COVID-19 Diagnosis on Chest CT Images

A novel coronavirus disease (COVID-19) has been a severe world threat to humans since December 2020. The virus mainly affects the human respiratory system, making breathing difficult. Early detection and Diagnosis are essential to controlling the disease. Radiological imaging, like Computed Tomograp...

Full description

Saved in:
Bibliographic Details
Published in:International journal of advanced computer science & applications 2022-01, Vol.13 (11)
Main Authors: S, Briskline Kiruba, D, Murugan, A, Petchiammal
Format: Article
Language:English
Subjects:
Abnormalities
Artificial intelligence
Chest
Computed tomography
Coronaviruses
COVID-19
Datasets
Deep learning
Diagnosis
Disease control
Image classification
Image quality
Machine learning
Medical imaging
Respiratory system
Signs and symptoms
Viral diseases
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:A novel coronavirus disease (COVID-19) has been a severe world threat to humans since December 2020. The virus mainly affects the human respiratory system, making breathing difficult. Early detection and Diagnosis are essential to controlling the disease. Radiological imaging, like Computed Tomography (CT) scans, produces clear, high-quality chest images and helps quickly diagnoses lung abnormalities. The recent advancements in Artificial intelligence enable accurate and fast detection of COVID-19 symptoms on chest CT images. This paper presents COVIDnet, an improved and efficient deep learning Model for COVID-19 diagnosis on chest CT images. We developed a chest CT dataset from 220 CT studies from Tamil Nadu, India, to evaluate the proposed model. The final dataset contains 5191 CT images (3820 COVID-infected and 1371 normal CT images). The proposed COVIDnet model aims to produce accurate diagnostics for classifying these two classes. Our experimental result shows that COVIDnet achieved a superior accuracy of 98.98% when compared with three contemporary deep learning models.
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2022.0131196