Classification of COVID-19 Patients Using Deep Learning Architecture of InceptionV3 and ResNet50

Muhammad Raihan; Muhammad Suryanegara

doi:10.1109/IC2IE53219.2021.9649255

Classification of COVID-19 Patients Using Deep Learning Architecture of InceptionV3 and ResNet50

Muhammad Raihan
, Muhammad Suryanegara

Department of Electrical Engineering

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

8 Citations (Scopus)

Abstract

This paper aims to compare the deep learning Convolutional Neural Network (CNN) model for a case study of 3 classes chest x-ray classification of patients with "COVID-19", "pneumonia", and "normal people"using 2 architectures, namely InceptionV3 and ResNet50. This model was created using the GoogleColab platform with the Python programming language. This comparison aims to find the best results using 4 evaluation metrics and several scenarios for dividing the number of datasets used for training and validation. The evaluation metrics used include accuracy, precision, recall, and F1-score. The best accuracy is generated on a model with the ResNet50 architecture with a training accuracy value of 98.62% and accuracy validation of 96.53%. While in the InceptionV3 architecture, the resulting value for training accuracy is 96.13% and accuracy validation is 91.52%.

Original language	English
Title of host publication	Proceedings - 2021 4th International Conference on Computer and Informatics Engineering
Subtitle of host publication	IT-Based Digital Industrial Innovation for the Welfare of Society, IC2IE 2021
Editors	Iklima Ermis Ismail, Indra Hermawan, Muhammad Yusuf Bagus Rasyidin, Malisa Huzaifa, Asep Taufik Muharram, Noorlela Marcheeta, Dewi Kurniawati, Ade Rahma Yuly, Maria Agustin, Rizki Elisa Nalawati, Dodon Turianto Nugrahadi, Irwan Budiman
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	46-50
Number of pages	5
ISBN (Electronic)	9781665442886
DOIs	https://doi.org/10.1109/IC2IE53219.2021.9649255
Publication status	Published - 2021
Event	4th International Conference on Computer and Informatics Engineering, IC2IE 2021 - Virutal, Depok, Indonesia Duration: 14 Sept 2021 → …

Publication series

Name	Proceedings - 2021 4th International Conference on Computer and Informatics Engineering: IT-Based Digital Industrial Innovation for the Welfare of Society, IC2IE 2021

Conference

Conference	4th International Conference on Computer and Informatics Engineering, IC2IE 2021
Country/Territory	Indonesia
City	Virutal, Depok
Period	14/09/21 → …

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

artificial intelligence
CNN
COVID-19
deep learning
inceptionV3
pneumonia
resNet50

Access to Document

10.1109/IC2IE53219.2021.9649255

Cite this

Raihan, M., & Suryanegara, M. (2021). Classification of COVID-19 Patients Using Deep Learning Architecture of InceptionV3 and ResNet50. In I. E. Ismail, I. Hermawan, M. Y. B. Rasyidin, M. Huzaifa, A. T. Muharram, N. Marcheeta, D. Kurniawati, A. R. Yuly, M. Agustin, R. E. Nalawati, D. T. Nugrahadi, & I. Budiman (Eds.), Proceedings - 2021 4th International Conference on Computer and Informatics Engineering: IT-Based Digital Industrial Innovation for the Welfare of Society, IC2IE 2021 (pp. 46-50). (Proceedings - 2021 4th International Conference on Computer and Informatics Engineering: IT-Based Digital Industrial Innovation for the Welfare of Society, IC2IE 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IC2IE53219.2021.9649255

Raihan, Muhammad ; Suryanegara, Muhammad. / Classification of COVID-19 Patients Using Deep Learning Architecture of InceptionV3 and ResNet50. Proceedings - 2021 4th International Conference on Computer and Informatics Engineering: IT-Based Digital Industrial Innovation for the Welfare of Society, IC2IE 2021. editor / Iklima Ermis Ismail ; Indra Hermawan ; Muhammad Yusuf Bagus Rasyidin ; Malisa Huzaifa ; Asep Taufik Muharram ; Noorlela Marcheeta ; Dewi Kurniawati ; Ade Rahma Yuly ; Maria Agustin ; Rizki Elisa Nalawati ; Dodon Turianto Nugrahadi ; Irwan Budiman. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 46-50 (Proceedings - 2021 4th International Conference on Computer and Informatics Engineering: IT-Based Digital Industrial Innovation for the Welfare of Society, IC2IE 2021).

@inproceedings{11359a2797704dfdaa6aa72f15ae3c56,

title = "Classification of COVID-19 Patients Using Deep Learning Architecture of InceptionV3 and ResNet50",

abstract = "This paper aims to compare the deep learning Convolutional Neural Network (CNN) model for a case study of 3 classes chest x-ray classification of patients with {"}COVID-19{"}, {"}pneumonia{"}, and {"}normal people{"}using 2 architectures, namely InceptionV3 and ResNet50. This model was created using the GoogleColab platform with the Python programming language. This comparison aims to find the best results using 4 evaluation metrics and several scenarios for dividing the number of datasets used for training and validation. The evaluation metrics used include accuracy, precision, recall, and F1-score. The best accuracy is generated on a model with the ResNet50 architecture with a training accuracy value of 98.62\% and accuracy validation of 96.53\%. While in the InceptionV3 architecture, the resulting value for training accuracy is 96.13\% and accuracy validation is 91.52\%. ",

keywords = "artificial intelligence, CNN, COVID-19, deep learning, inceptionV3, pneumonia, resNet50",

author = "Muhammad Raihan and Muhammad Suryanegara",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 4th International Conference on Computer and Informatics Engineering, IC2IE 2021 ; Conference date: 14-09-2021",

year = "2021",

doi = "10.1109/IC2IE53219.2021.9649255",

language = "English",

series = "Proceedings - 2021 4th International Conference on Computer and Informatics Engineering: IT-Based Digital Industrial Innovation for the Welfare of Society, IC2IE 2021",

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

pages = "46--50",

editor = "Ismail, \{Iklima Ermis\} and Indra Hermawan and Rasyidin, \{Muhammad Yusuf Bagus\} and Malisa Huzaifa and Muharram, \{Asep Taufik\} and Noorlela Marcheeta and Dewi Kurniawati and Yuly, \{Ade Rahma\} and Maria Agustin and Nalawati, \{Rizki Elisa\} and Nugrahadi, \{Dodon Turianto\} and Irwan Budiman",

booktitle = "Proceedings - 2021 4th International Conference on Computer and Informatics Engineering",

address = "United States",

}

Raihan, M & Suryanegara, M 2021, Classification of COVID-19 Patients Using Deep Learning Architecture of InceptionV3 and ResNet50. in IE Ismail, I Hermawan, MYB Rasyidin, M Huzaifa, AT Muharram, N Marcheeta, D Kurniawati, AR Yuly, M Agustin, RE Nalawati, DT Nugrahadi & I Budiman (eds), Proceedings - 2021 4th International Conference on Computer and Informatics Engineering: IT-Based Digital Industrial Innovation for the Welfare of Society, IC2IE 2021. Proceedings - 2021 4th International Conference on Computer and Informatics Engineering: IT-Based Digital Industrial Innovation for the Welfare of Society, IC2IE 2021, Institute of Electrical and Electronics Engineers Inc., pp. 46-50, 4th International Conference on Computer and Informatics Engineering, IC2IE 2021, Virutal, Depok, Indonesia, 14/09/21. https://doi.org/10.1109/IC2IE53219.2021.9649255

Classification of COVID-19 Patients Using Deep Learning Architecture of InceptionV3 and ResNet50. / Raihan, Muhammad; Suryanegara, Muhammad.
Proceedings - 2021 4th International Conference on Computer and Informatics Engineering: IT-Based Digital Industrial Innovation for the Welfare of Society, IC2IE 2021. ed. / Iklima Ermis Ismail; Indra Hermawan; Muhammad Yusuf Bagus Rasyidin; Malisa Huzaifa; Asep Taufik Muharram; Noorlela Marcheeta; Dewi Kurniawati; Ade Rahma Yuly; Maria Agustin; Rizki Elisa Nalawati; Dodon Turianto Nugrahadi; Irwan Budiman. Institute of Electrical and Electronics Engineers Inc., 2021. p. 46-50 (Proceedings - 2021 4th International Conference on Computer and Informatics Engineering: IT-Based Digital Industrial Innovation for the Welfare of Society, IC2IE 2021).

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

TY - GEN

T1 - Classification of COVID-19 Patients Using Deep Learning Architecture of InceptionV3 and ResNet50

AU - Raihan, Muhammad

AU - Suryanegara, Muhammad

PY - 2021

Y1 - 2021

N2 - This paper aims to compare the deep learning Convolutional Neural Network (CNN) model for a case study of 3 classes chest x-ray classification of patients with "COVID-19", "pneumonia", and "normal people"using 2 architectures, namely InceptionV3 and ResNet50. This model was created using the GoogleColab platform with the Python programming language. This comparison aims to find the best results using 4 evaluation metrics and several scenarios for dividing the number of datasets used for training and validation. The evaluation metrics used include accuracy, precision, recall, and F1-score. The best accuracy is generated on a model with the ResNet50 architecture with a training accuracy value of 98.62% and accuracy validation of 96.53%. While in the InceptionV3 architecture, the resulting value for training accuracy is 96.13% and accuracy validation is 91.52%.

AB - This paper aims to compare the deep learning Convolutional Neural Network (CNN) model for a case study of 3 classes chest x-ray classification of patients with "COVID-19", "pneumonia", and "normal people"using 2 architectures, namely InceptionV3 and ResNet50. This model was created using the GoogleColab platform with the Python programming language. This comparison aims to find the best results using 4 evaluation metrics and several scenarios for dividing the number of datasets used for training and validation. The evaluation metrics used include accuracy, precision, recall, and F1-score. The best accuracy is generated on a model with the ResNet50 architecture with a training accuracy value of 98.62% and accuracy validation of 96.53%. While in the InceptionV3 architecture, the resulting value for training accuracy is 96.13% and accuracy validation is 91.52%.

KW - artificial intelligence

KW - CNN

KW - COVID-19

KW - deep learning

KW - inceptionV3

KW - pneumonia

KW - resNet50

UR - https://www.scopus.com/pages/publications/85124268992

U2 - 10.1109/IC2IE53219.2021.9649255

DO - 10.1109/IC2IE53219.2021.9649255

M3 - Conference contribution

AN - SCOPUS:85124268992

T3 - Proceedings - 2021 4th International Conference on Computer and Informatics Engineering: IT-Based Digital Industrial Innovation for the Welfare of Society, IC2IE 2021

SP - 46

EP - 50

BT - Proceedings - 2021 4th International Conference on Computer and Informatics Engineering

A2 - Ismail, Iklima Ermis

A2 - Hermawan, Indra

A2 - Rasyidin, Muhammad Yusuf Bagus

A2 - Huzaifa, Malisa

A2 - Muharram, Asep Taufik

A2 - Marcheeta, Noorlela

A2 - Kurniawati, Dewi

A2 - Yuly, Ade Rahma

A2 - Agustin, Maria

A2 - Nalawati, Rizki Elisa

A2 - Nugrahadi, Dodon Turianto

A2 - Budiman, Irwan

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 4th International Conference on Computer and Informatics Engineering, IC2IE 2021

Y2 - 14 September 2021

ER -

Raihan M, Suryanegara M. Classification of COVID-19 Patients Using Deep Learning Architecture of InceptionV3 and ResNet50. In Ismail IE, Hermawan I, Rasyidin MYB, Huzaifa M, Muharram AT, Marcheeta N, Kurniawati D, Yuly AR, Agustin M, Nalawati RE, Nugrahadi DT, Budiman I, editors, Proceedings - 2021 4th International Conference on Computer and Informatics Engineering: IT-Based Digital Industrial Innovation for the Welfare of Society, IC2IE 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 46-50. (Proceedings - 2021 4th International Conference on Computer and Informatics Engineering: IT-Based Digital Industrial Innovation for the Welfare of Society, IC2IE 2021). doi: 10.1109/IC2IE53219.2021.9649255

Classification of COVID-19 Patients Using Deep Learning Architecture of InceptionV3 and ResNet50

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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