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
N1 - Publisher Copyright:
© 2023 IEEE.
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 - http://www.scopus.com/inward/record.url?scp=85187408293&partnerID=8YFLogxK
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 -
