Detection of Covid-19 on X-Ray Image of Human Chest Using CNN and
Transfer Learning
Dublin Core
Title
Detection of Covid-19 on X-Ray Image of Human Chest Using CNN and
Transfer Learning
Transfer Learning
Subject
COVID-19, Image Classification, Convolutional Neural Network, Transfer Learning
Description
At the end of 2019, a new disease called Coronavirus Disease (COVID-19) originated in Wuhan, China. This disease is caused
by respiratory tract infections, ranging from the common cold to serious diseases such as Middle East Respiratory Syndrome
(MERS) and Severe Acute Respiratory Syndrome (SARS). In Indonesia, there are tests to detect COVID-19, such as PCR and
Rapid Test. This detector takes a long time and is less accurate in producing a diagnosis. This study aims to classify chest Xray images using CNN and Transfer Learning methods to diagnose COVID-19. The proposed model has 4 scenarios: CNN
Handcraft Model, Transfer Learning (VGG 16, VGG 19, and ResNet 50). This model is accompanied by data augmentation
and data balancing techniques using undersampling techniques. The dataset used in this study is the “Covid-19 (COVID-19
and Normal) Radiographic Database” with 13,808 data divided into two classes, namely COVID-19 and Normal. Each model
built will produce values for accuracy, precision, recall, and confusion matrix. The results of CNN Scenario 1 accuracy is
95%, in Scenario 2 VGG 16 the accuracy is 93%, Scenario 3 VGG 19 is 90% and Scenario 4 ResNet 50 is 80%.
by respiratory tract infections, ranging from the common cold to serious diseases such as Middle East Respiratory Syndrome
(MERS) and Severe Acute Respiratory Syndrome (SARS). In Indonesia, there are tests to detect COVID-19, such as PCR and
Rapid Test. This detector takes a long time and is less accurate in producing a diagnosis. This study aims to classify chest Xray images using CNN and Transfer Learning methods to diagnose COVID-19. The proposed model has 4 scenarios: CNN
Handcraft Model, Transfer Learning (VGG 16, VGG 19, and ResNet 50). This model is accompanied by data augmentation
and data balancing techniques using undersampling techniques. The dataset used in this study is the “Covid-19 (COVID-19
and Normal) Radiographic Database” with 13,808 data divided into two classes, namely COVID-19 and Normal. Each model
built will produce values for accuracy, precision, recall, and confusion matrix. The results of CNN Scenario 1 accuracy is
95%, in Scenario 2 VGG 16 the accuracy is 93%, Scenario 3 VGG 19 is 90% and Scenario 4 ResNet 50 is 80%.
Creator
Jalu Nusantoro¹, Faldo Fajri Afrinanto², Wana Salma Labibah³, Zamah Sari4
, Yufis Azhar5
, Yufis Azhar5
Publisher
University of Muhammadiyah Malang
Date
30-06-2022
Contributor
Fajar bagus W
Format
PDF
Language
Indonesia
Type
Text
Files
Collection
Citation
Jalu Nusantoro¹, Faldo Fajri Afrinanto², Wana Salma Labibah³, Zamah Sari4
, Yufis Azhar5, “Detection of Covid-19 on X-Ray Image of Human Chest Using CNN and
Transfer Learning,” Repository Horizon University Indonesia, accessed June 6, 2025, https://repository.horizon.ac.id/items/show/9187.
Transfer Learning,” Repository Horizon University Indonesia, accessed June 6, 2025, https://repository.horizon.ac.id/items/show/9187.