TY - GEN
T1 - Classification of COVID-19 Patients Using Deep Learning Architecture of InceptionV3 and ResNet50
AU - Raihan, Muhammad
AU - Suryanegara, Muhammad
N1 - Publisher Copyright:
© 2021 IEEE.
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 - http://www.scopus.com/inward/record.url?scp=85124268992&partnerID=8YFLogxK
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 -