An effective Approach for Pneumonia Detection using Convolution Vision Transformer

Title

An effective Approach for Pneumonia Detection using Convolution Vision Transformer

Creator

Gupta S.; Rodrigues A.; Joshi P.; George J.

Description

Early detection of pneumonia in patients through effective medical imaging may enable timely remedial measures and reduce the severity of the infection. There is an increase in cases among new-borns, teenagers and also people with health issues in recent years. The COVID-19 pandemic also revealed the major impact pneumonia had on the lungs and the consequences of delayed detection. The presence of the infection in the lungs is examined through images of Chest X-ray, however, for an early diagnosis of the infection, this paper proposes an automated model as a more effective alternative. Convolutional Vision Transformer (CVT) which gives an accuracy of 97.13%, and is a robust combination of Convolution and Vision Transformer (ViT), is suggested in this paper as a potential model to detect pneumonia early in patients. 2022 IEEE.

Source

2022 International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2022

Date

2022-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Chest X-ray; Convolution Neural Network; Convolutional Vision Transformer; Deep Learning; Detection; Pneumonia; Vision Transformation

Coverage

Gupta S., CHRIST (Deemed to Be University), India; Rodrigues A., CHRIST (Deemed to Be University), India; Joshi P., CHRIST (Deemed to Be University), India; George J., CHRIST (Deemed to Be University), India

Rights

Restricted Access

Relation

ISBN: 978-166545361-5

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/TQCEBT54229.2022.10041662

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149182726&doi=10.1109%2fTQCEBT54229.2022.10041662&partnerID=40&md5=15acdaed24c16c6424566f6ee591b628