C-LVQ: A Convolutional Neural Network with Learning Vector Quantization for the Diagnosis of Covid-19

Naveen Gehlot; Ankit Vijayvargiya; Rajesh Kumar; Akhil Ranjan Garg; Usha Desai

doi:10.1109/INDICON59947.2023.10440796

C-LVQ: A Convolutional Neural Network with Learning Vector Quantization for the Diagnosis of Covid-19

Naveen Gehlot
, Ankit Vijayvargiya
, Rajesh Kumar
, Akhil Ranjan Garg
, Usha Desai

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

Globally, the spread of Covid-19 started in December 2019 and led to world chaos. For the detection of Covid-19, the standard Reverse Transcription Polymerase Chain Reaction (RT-PCR) test is famous for the initial diagnosis. This standard RT-PCR test has limitations, such as being time consuming, having low sensitivity, etc. Chest X-Rays (CXR) and CT scans may also detect lung infections, which can aid doctors in detecting Covid-19. The detection of Covid-19 using CXR via artificial intelligence based diagnosis is more efficient and accurate than traditional medical practice. For the automated diagnosis of Covid-19, a hybrid of Convolutional Neural Network and Learning Vector Quantization (C-LVQ) is proposed. First, five pre-trained Convolutional Neural Network (CNN) models are selected for feature extraction, followed by Learning Vector Quantization (LVQ) for classification between Covid-19, Pneumonia, and Healthy subjects. The results show that of all the hybrid networks studied, the MobileNetV2-LVQ, a hybrid of the MobileNetV2 architecture of CNN and LVQ, has the highest accuracy of 91.61%.

Original language	English
Title of host publication	2023 IEEE 20th India Council International Conference, INDICON 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	55-60
Number of pages	6
ISBN (Electronic)	9798350305593
DOIs	https://doi.org/10.1109/INDICON59947.2023.10440796
Publication status	Published - 2023
Externally published	Yes
Event	20th IEEE India Council International Conference, INDICON 2023 - Hyderabad, India Duration: 14 Dec 2023 → 17 Dec 2023

Publication series

Name	2023 IEEE 20th India Council International Conference, INDICON 2023

Conference

Conference	20th IEEE India Council International Conference, INDICON 2023
Country/Territory	India
City	Hyderabad
Period	14/12/23 → 17/12/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Chest X-Ray (CXR)
Convolutional Neural Network (CNN)
Covid-19
Learning Vector Quantization (LVQ)
Reverse Transcription Polymerase Chain Reaction (RT-PCR)

ASJC Scopus subject areas

Hardware and Architecture
Signal Processing
Information Systems and Management
Energy Engineering and Power Technology
Instrumentation

Access to Document

10.1109/INDICON59947.2023.10440796

Cite this

Gehlot, N., Vijayvargiya, A., Kumar, R., Garg, A. R., & Desai, U. (2023). C-LVQ: A Convolutional Neural Network with Learning Vector Quantization for the Diagnosis of Covid-19. In 2023 IEEE 20th India Council International Conference, INDICON 2023 (pp. 55-60). (2023 IEEE 20th India Council International Conference, INDICON 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/INDICON59947.2023.10440796

Gehlot, Naveen ; Vijayvargiya, Ankit ; Kumar, Rajesh et al. / C-LVQ : A Convolutional Neural Network with Learning Vector Quantization for the Diagnosis of Covid-19. 2023 IEEE 20th India Council International Conference, INDICON 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 55-60 (2023 IEEE 20th India Council International Conference, INDICON 2023).

@inproceedings{b9706c8cda444cc88c81251aaf3aaa92,

title = "C-LVQ: A Convolutional Neural Network with Learning Vector Quantization for the Diagnosis of Covid-19",

abstract = "Globally, the spread of Covid-19 started in December 2019 and led to world chaos. For the detection of Covid-19, the standard Reverse Transcription Polymerase Chain Reaction (RT-PCR) test is famous for the initial diagnosis. This standard RT-PCR test has limitations, such as being time consuming, having low sensitivity, etc. Chest X-Rays (CXR) and CT scans may also detect lung infections, which can aid doctors in detecting Covid-19. The detection of Covid-19 using CXR via artificial intelligence based diagnosis is more efficient and accurate than traditional medical practice. For the automated diagnosis of Covid-19, a hybrid of Convolutional Neural Network and Learning Vector Quantization (C-LVQ) is proposed. First, five pre-trained Convolutional Neural Network (CNN) models are selected for feature extraction, followed by Learning Vector Quantization (LVQ) for classification between Covid-19, Pneumonia, and Healthy subjects. The results show that of all the hybrid networks studied, the MobileNetV2-LVQ, a hybrid of the MobileNetV2 architecture of CNN and LVQ, has the highest accuracy of 91.61\%.",

keywords = "Chest X-Ray (CXR), Convolutional Neural Network (CNN), Covid-19, Learning Vector Quantization (LVQ), Reverse Transcription Polymerase Chain Reaction (RT-PCR)",

author = "Naveen Gehlot and Ankit Vijayvargiya and Rajesh Kumar and Garg, \{Akhil Ranjan\} and Usha Desai",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 20th IEEE India Council International Conference, INDICON 2023 ; Conference date: 14-12-2023 Through 17-12-2023",

year = "2023",

doi = "10.1109/INDICON59947.2023.10440796",

language = "English",

series = "2023 IEEE 20th India Council International Conference, INDICON 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "55--60",

booktitle = "2023 IEEE 20th India Council International Conference, INDICON 2023",

address = "United States",

}

Gehlot, N, Vijayvargiya, A, Kumar, R, Garg, AR & Desai, U 2023, C-LVQ: A Convolutional Neural Network with Learning Vector Quantization for the Diagnosis of Covid-19. in 2023 IEEE 20th India Council International Conference, INDICON 2023. 2023 IEEE 20th India Council International Conference, INDICON 2023, Institute of Electrical and Electronics Engineers Inc., pp. 55-60, 20th IEEE India Council International Conference, INDICON 2023, Hyderabad, India, 14/12/23. https://doi.org/10.1109/INDICON59947.2023.10440796

C-LVQ: A Convolutional Neural Network with Learning Vector Quantization for the Diagnosis of Covid-19. / Gehlot, Naveen; Vijayvargiya, Ankit; Kumar, Rajesh et al.
2023 IEEE 20th India Council International Conference, INDICON 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 55-60 (2023 IEEE 20th India Council International Conference, INDICON 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - C-LVQ

T2 - 20th IEEE India Council International Conference, INDICON 2023

AU - Gehlot, Naveen

AU - Vijayvargiya, Ankit

AU - Kumar, Rajesh

AU - Garg, Akhil Ranjan

AU - Desai, Usha

PY - 2023

Y1 - 2023

N2 - Globally, the spread of Covid-19 started in December 2019 and led to world chaos. For the detection of Covid-19, the standard Reverse Transcription Polymerase Chain Reaction (RT-PCR) test is famous for the initial diagnosis. This standard RT-PCR test has limitations, such as being time consuming, having low sensitivity, etc. Chest X-Rays (CXR) and CT scans may also detect lung infections, which can aid doctors in detecting Covid-19. The detection of Covid-19 using CXR via artificial intelligence based diagnosis is more efficient and accurate than traditional medical practice. For the automated diagnosis of Covid-19, a hybrid of Convolutional Neural Network and Learning Vector Quantization (C-LVQ) is proposed. First, five pre-trained Convolutional Neural Network (CNN) models are selected for feature extraction, followed by Learning Vector Quantization (LVQ) for classification between Covid-19, Pneumonia, and Healthy subjects. The results show that of all the hybrid networks studied, the MobileNetV2-LVQ, a hybrid of the MobileNetV2 architecture of CNN and LVQ, has the highest accuracy of 91.61%.

AB - Globally, the spread of Covid-19 started in December 2019 and led to world chaos. For the detection of Covid-19, the standard Reverse Transcription Polymerase Chain Reaction (RT-PCR) test is famous for the initial diagnosis. This standard RT-PCR test has limitations, such as being time consuming, having low sensitivity, etc. Chest X-Rays (CXR) and CT scans may also detect lung infections, which can aid doctors in detecting Covid-19. The detection of Covid-19 using CXR via artificial intelligence based diagnosis is more efficient and accurate than traditional medical practice. For the automated diagnosis of Covid-19, a hybrid of Convolutional Neural Network and Learning Vector Quantization (C-LVQ) is proposed. First, five pre-trained Convolutional Neural Network (CNN) models are selected for feature extraction, followed by Learning Vector Quantization (LVQ) for classification between Covid-19, Pneumonia, and Healthy subjects. The results show that of all the hybrid networks studied, the MobileNetV2-LVQ, a hybrid of the MobileNetV2 architecture of CNN and LVQ, has the highest accuracy of 91.61%.

KW - Chest X-Ray (CXR)

KW - Convolutional Neural Network (CNN)

KW - Covid-19

KW - Learning Vector Quantization (LVQ)

KW - Reverse Transcription Polymerase Chain Reaction (RT-PCR)

UR - https://www.scopus.com/pages/publications/85187408293

U2 - 10.1109/INDICON59947.2023.10440796

DO - 10.1109/INDICON59947.2023.10440796

M3 - Conference contribution

AN - SCOPUS:85187408293

T3 - 2023 IEEE 20th India Council International Conference, INDICON 2023

SP - 55

EP - 60

BT - 2023 IEEE 20th India Council International Conference, INDICON 2023

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 14 December 2023 through 17 December 2023

ER -

Gehlot N, Vijayvargiya A, Kumar R, Garg AR, Desai U. C-LVQ: A Convolutional Neural Network with Learning Vector Quantization for the Diagnosis of Covid-19. In 2023 IEEE 20th India Council International Conference, INDICON 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 55-60. (2023 IEEE 20th India Council International Conference, INDICON 2023). doi: 10.1109/INDICON59947.2023.10440796

C-LVQ: A Convolutional Neural Network with Learning Vector Quantization for the Diagnosis of Covid-19

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

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