Deep Transfer Learning Model-Based Automated Detection of COVID-19 from X-ray Images and Interpretation of COVID-19 Images Using GLCM Texture Features

Shilpa Ankalaki; Kartikeya Shorya; Jharna Majumdar

doi:10.1007/978-981-16-1342-5_45

Deep Transfer Learning Model-Based Automated Detection of COVID-19 from X-ray Images and Interpretation of COVID-19 Images Using GLCM Texture Features

Shilpa Ankalaki^*
, Kartikeya Shorya
, Jharna Majumdar

^*Corresponding author for this work

School of Computer Engineering

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

Abstract

The novel coronavirus 2019 (COVID-2019), which initially proved its existence in Wuhan city of China in December 2019, spread quickly around the globe and turned into a pandemic. It has caused a staggering impact on all the sectors of the world like public health, global economy and daily lives. COVID-19 positive cases and death due to COVID-19 are rapidly increasing day by day. It is crucial and essential for fast and accurate automatic detection of COVID-19 infection to make better decisions and to provide appropriate treatment for the patients that can hopefully save their lives. The current has employed the VGG19, RESNET50 and DesNet121 deep learning convolutional neural network with transfer learning to identify and classifies the X-ray images into COVID-19 and non-COVID-19 classes. This study has been extended to analyse the factors which distinguishes COVID-19 and non-COVID-19 images. To accomplish this task, we have employed GLCM features and determined that variance is the best feature for this purpose. GRAD-CAM algorithm has been used to interpret the decision of CNN architecture. In this study, VGG19 and DenseNet121 achieved the classification accuracy of 98.80%, and ResNet50 achieved the accuracy of 97.65% for binary classes (COVID-19 and non-COVID-19 classes).

Original language	English
Title of host publication	Emerging Research in Computing, Information, Communication and Applications, ERCICA 2020
Editors	N. R. Shetty, L. M. Patnaik, H. C. Nagaraj, Prasad N. Hamsavath, N. Nalini
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	581-598
Number of pages	18
ISBN (Print)	9789811613418
DOIs	https://doi.org/10.1007/978-981-16-1342-5_45
Publication status	Published - 2022
Event	6th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2020 - Bangalore, India Duration: 25-09-2020 → 26-09-2020

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	790
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	6th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2020
Country/Territory	India
City	Bangalore
Period	25-09-20 → 26-09-20

UN SDGs

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

SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

Industrial and Manufacturing Engineering

Access to Document

10.1007/978-981-16-1342-5_45

Cite this

Ankalaki, S., Shorya, K., & Majumdar, J. (2022). Deep Transfer Learning Model-Based Automated Detection of COVID-19 from X-ray Images and Interpretation of COVID-19 Images Using GLCM Texture Features. In N. R. Shetty, L. M. Patnaik, H. C. Nagaraj, P. N. Hamsavath, & N. Nalini (Eds.), Emerging Research in Computing, Information, Communication and Applications, ERCICA 2020 (pp. 581-598). (Lecture Notes in Electrical Engineering; Vol. 790). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-16-1342-5_45

Ankalaki, Shilpa ; Shorya, Kartikeya ; Majumdar, Jharna. / Deep Transfer Learning Model-Based Automated Detection of COVID-19 from X-ray Images and Interpretation of COVID-19 Images Using GLCM Texture Features. Emerging Research in Computing, Information, Communication and Applications, ERCICA 2020. editor / N. R. Shetty ; L. M. Patnaik ; H. C. Nagaraj ; Prasad N. Hamsavath ; N. Nalini. Springer Science and Business Media Deutschland GmbH, 2022. pp. 581-598 (Lecture Notes in Electrical Engineering).

@inproceedings{9b575898bc7a48159fcc46b2380e2244,

title = "Deep Transfer Learning Model-Based Automated Detection of COVID-19 from X-ray Images and Interpretation of COVID-19 Images Using GLCM Texture Features",

abstract = "The novel coronavirus 2019 (COVID-2019), which initially proved its existence in Wuhan city of China in December 2019, spread quickly around the globe and turned into a pandemic. It has caused a staggering impact on all the sectors of the world like public health, global economy and daily lives. COVID-19 positive cases and death due to COVID-19 are rapidly increasing day by day. It is crucial and essential for fast and accurate automatic detection of COVID-19 infection to make better decisions and to provide appropriate treatment for the patients that can hopefully save their lives. The current has employed the VGG19, RESNET50 and DesNet121 deep learning convolutional neural network with transfer learning to identify and classifies the X-ray images into COVID-19 and non-COVID-19 classes. This study has been extended to analyse the factors which distinguishes COVID-19 and non-COVID-19 images. To accomplish this task, we have employed GLCM features and determined that variance is the best feature for this purpose. GRAD-CAM algorithm has been used to interpret the decision of CNN architecture. In this study, VGG19 and DenseNet121 achieved the classification accuracy of 98.80\%, and ResNet50 achieved the accuracy of 97.65\% for binary classes (COVID-19 and non-COVID-19 classes).",

author = "Shilpa Ankalaki and Kartikeya Shorya and Jharna Majumdar",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 6th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2020 ; Conference date: 25-09-2020 Through 26-09-2020",

year = "2022",

doi = "10.1007/978-981-16-1342-5\_45",

language = "English",

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editor = "Shetty, \{N. R.\} and Patnaik, \{L. M.\} and Nagaraj, \{H. C.\} and Hamsavath, \{Prasad N.\} and N. Nalini",

booktitle = "Emerging Research in Computing, Information, Communication and Applications, ERCICA 2020",

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Ankalaki, S, Shorya, K & Majumdar, J 2022, Deep Transfer Learning Model-Based Automated Detection of COVID-19 from X-ray Images and Interpretation of COVID-19 Images Using GLCM Texture Features. in NR Shetty, LM Patnaik, HC Nagaraj, PN Hamsavath & N Nalini (eds), Emerging Research in Computing, Information, Communication and Applications, ERCICA 2020. Lecture Notes in Electrical Engineering, vol. 790, Springer Science and Business Media Deutschland GmbH, pp. 581-598, 6th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2020, Bangalore, India, 25-09-20. https://doi.org/10.1007/978-981-16-1342-5_45

Deep Transfer Learning Model-Based Automated Detection of COVID-19 from X-ray Images and Interpretation of COVID-19 Images Using GLCM Texture Features. / Ankalaki, Shilpa; Shorya, Kartikeya; Majumdar, Jharna.
Emerging Research in Computing, Information, Communication and Applications, ERCICA 2020. ed. / N. R. Shetty; L. M. Patnaik; H. C. Nagaraj; Prasad N. Hamsavath; N. Nalini. Springer Science and Business Media Deutschland GmbH, 2022. p. 581-598 (Lecture Notes in Electrical Engineering; Vol. 790).

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

TY - GEN

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AU - Ankalaki, Shilpa

AU - Shorya, Kartikeya

AU - Majumdar, Jharna

PY - 2022

Y1 - 2022

N2 - The novel coronavirus 2019 (COVID-2019), which initially proved its existence in Wuhan city of China in December 2019, spread quickly around the globe and turned into a pandemic. It has caused a staggering impact on all the sectors of the world like public health, global economy and daily lives. COVID-19 positive cases and death due to COVID-19 are rapidly increasing day by day. It is crucial and essential for fast and accurate automatic detection of COVID-19 infection to make better decisions and to provide appropriate treatment for the patients that can hopefully save their lives. The current has employed the VGG19, RESNET50 and DesNet121 deep learning convolutional neural network with transfer learning to identify and classifies the X-ray images into COVID-19 and non-COVID-19 classes. This study has been extended to analyse the factors which distinguishes COVID-19 and non-COVID-19 images. To accomplish this task, we have employed GLCM features and determined that variance is the best feature for this purpose. GRAD-CAM algorithm has been used to interpret the decision of CNN architecture. In this study, VGG19 and DenseNet121 achieved the classification accuracy of 98.80%, and ResNet50 achieved the accuracy of 97.65% for binary classes (COVID-19 and non-COVID-19 classes).

AB - The novel coronavirus 2019 (COVID-2019), which initially proved its existence in Wuhan city of China in December 2019, spread quickly around the globe and turned into a pandemic. It has caused a staggering impact on all the sectors of the world like public health, global economy and daily lives. COVID-19 positive cases and death due to COVID-19 are rapidly increasing day by day. It is crucial and essential for fast and accurate automatic detection of COVID-19 infection to make better decisions and to provide appropriate treatment for the patients that can hopefully save their lives. The current has employed the VGG19, RESNET50 and DesNet121 deep learning convolutional neural network with transfer learning to identify and classifies the X-ray images into COVID-19 and non-COVID-19 classes. This study has been extended to analyse the factors which distinguishes COVID-19 and non-COVID-19 images. To accomplish this task, we have employed GLCM features and determined that variance is the best feature for this purpose. GRAD-CAM algorithm has been used to interpret the decision of CNN architecture. In this study, VGG19 and DenseNet121 achieved the classification accuracy of 98.80%, and ResNet50 achieved the accuracy of 97.65% for binary classes (COVID-19 and non-COVID-19 classes).

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T3 - Lecture Notes in Electrical Engineering

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Ankalaki S, Shorya K, Majumdar J. Deep Transfer Learning Model-Based Automated Detection of COVID-19 from X-ray Images and Interpretation of COVID-19 Images Using GLCM Texture Features. In Shetty NR, Patnaik LM, Nagaraj HC, Hamsavath PN, Nalini N, editors, Emerging Research in Computing, Information, Communication and Applications, ERCICA 2020. Springer Science and Business Media Deutschland GmbH. 2022. p. 581-598. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-16-1342-5_45

Deep Transfer Learning Model-Based Automated Detection of COVID-19 from X-ray Images and Interpretation of COVID-19 Images Using GLCM Texture Features

Abstract

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