CoviExpert: COVID-19 detection from chest X-ray using CNN.

CNN COVID-19 CT scan CoviExpert Deep learning X-ray

Journal

Measurement. Sensors
ISSN: 2665-9174
Titre abrégé: Measur Sens
Pays: England
ID NLM: 9918506286206676

Informations de publication

Date de publication:
Oct 2022
Historique:
received: 30 05 2022
revised: 12 07 2022
accepted: 13 07 2022
entrez: 10 3 2023
pubmed: 11 3 2023
medline: 11 3 2023
Statut: ppublish

Résumé

COVID-19 continues to threaten the world with its impact and severity. This pandemic has created a sense of havoc and shook the world stretching the medical fraternity to an unimaginable extent, who are now facing fatigue and exhaustion. Due to the rapid increase in cases all across the globe demanding extensive medical care, people are hunting for resources like testing facilities, medical drugs and even hospital beds. Even people with mild to moderate infection are panicking and mentally giving up due to anxiety and desperation. To combat these issues, it is necessary to find an inexpensive and faster way to save lives and bring about a much-needed change. The most fundamental way through which this can be achieved is with the help of radiology which involves examination of Chest X rays. They are primarily used for the diagnosis of this disease. But due to panic and severity of this disease a recent trend of performing CT scans has been observed. This has been under scrutiny since it exposes patients to a very high level of radiation known to increase the probability of cancer. As quoted by the AIIMS Director, one CT scan is equivalent to around 300-400 Chest X-rays. Also, it is relatively a much costlier testing method. Hence, in this report, we have presented a Deep learning approach which can detect covid 19 positive cases from Chest X ray images. It involves creation of a Deep learning based Convolutional Neural Network (CNN) using Keras (python library) and integrating the model with a front-end user interface for ease of use. This leads up to the creation of a software which we have named as CoviExpert. It uses the sequential Keras model which is built layer by layer. All the layers are trained independently to make independent predictions which are then combined to give the final output. 1584 images of Chest X-rays of both COVID-19 positive and negative patients have been used as training data. 177 images have been used as testing data. The proposed approach gives a classification accuracy of 99%. CoviExpert can be used on any device by any medical professional to detect Covid positive patients within a few seconds.

Identifiants

pubmed: 36896335
doi: 10.1016/j.measen.2022.100392
pii: S2665-9174(22)00026-5
pmc: PMC9344736
doi:

Types de publication

Journal Article

Langues

eng

Pagination

100392

Informations de copyright

© 2022 The Authors.

Déclaration de conflit d'intérêts

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Références

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pubmed: 33967656

Auteurs

A Arivoli (A)

School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India.

Devdatt Golwala (D)

School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India.

Rayirth Reddy (R)

School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India.

Classifications MeSH