TY - GEN
T1 - The Diagnosis of COVID-19 Through X-Ray Images via Transfer Learning Pipeline
AU - Ismail, Amiir Haamzah Mohamed
AU - Abdullah, Muhammad Amirul
AU - Khairuddin, Ismail Mohd
AU - Mohd Isa, Wan Hasbullah
AU - Razman, Mohd Azraai Mohd
AU - Jizat, Jessnor Arif Mat
AU - P. P. Abdul Majeed, Anwar
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Transfer Learning (TL) opens new possibilities of detection of disease through radiography as compared to conventional machine learning as well as deep learning methods. The extraction of features through pre-trained Convolutional Neural Networks (CNN) and the tuning of the fully connected layers of the CNN model is the core for the development of a transfer learning pipeline. The present study investigates the diagnosis of COVID-19 through X-ray images by means of three TL models, namely Inception V3, VGG-16, and the VGG-19 for feature extraction along with heuristically fine-tuned fully connected layers. It was demonstrated through this preliminary work that both the VGG-16 and VGG-19 tuned pipelines could achieve a train and test classification accuracies of 99.8% and 94%, respectively.
AB - Transfer Learning (TL) opens new possibilities of detection of disease through radiography as compared to conventional machine learning as well as deep learning methods. The extraction of features through pre-trained Convolutional Neural Networks (CNN) and the tuning of the fully connected layers of the CNN model is the core for the development of a transfer learning pipeline. The present study investigates the diagnosis of COVID-19 through X-ray images by means of three TL models, namely Inception V3, VGG-16, and the VGG-19 for feature extraction along with heuristically fine-tuned fully connected layers. It was demonstrated through this preliminary work that both the VGG-16 and VGG-19 tuned pipelines could achieve a train and test classification accuracies of 99.8% and 94%, respectively.
KW - COVID-19
KW - Inception v3
KW - Transfer learning
KW - VGG-16
KW - VGG-19
KW - X-ray Images
UR - https://www.scopus.com/pages/publications/85104490912
U2 - 10.1007/978-3-030-70917-4_36
DO - 10.1007/978-3-030-70917-4_36
M3 - Conference Proceeding
AN - SCOPUS:85104490912
SN - 9783030709167
T3 - Advances in Intelligent Systems and Computing
SP - 378
EP - 384
BT - Advances in Robotics, Automation and Data Analytics - Selected Papers from iCITES 2020
A2 - Mat Jizat, Jessnor Arif
A2 - Khairuddin, Ismail Mohd
A2 - Mohd Razman, Mohd Azraai
A2 - Ab. Nasir, Ahmad Fakhri
A2 - Abdul Karim, Mohamad Shaiful
A2 - Jaafar, Abdul Aziz
A2 - Hong, Lim Wei
A2 - Abdul Majeed, Anwar P.
A2 - Liu, Pengcheng
A2 - Myung, Hyun
A2 - Choi, Han-Lim
A2 - Susto, Gian-Antonio
PB - Springer Science and Business Media Deutschland GmbH
T2 - 2nd International Conference on Innovative Technology, Engineering and Sciences, iCITES 2020
Y2 - 22 December 2020 through 22 December 2020
ER -