Deteksi Penyakit Covid-19 Pada Citra X-Ray Dengan Pendekatan
Convolutional Neural Network (CNN)

Deteksi Penyakit Covid-19 Pada Citra X-Ray Dengan Pendekatan
Convolutional Neural Network (CNN)

COVID-19, convolutional neural network, CNN, Residual Network, ResNet

The Coronavirus (COVID-19) pandemic has resulted in the worldwide death rate continuing to increase significantly,
identification using medical imaging such as X-rays and computed tomography plays an important role in helping medical
personnel diagnose positive negative COVID-19 patients, several works have proven the learning approach in-depth using a
Convolutional Neural Network (CNN) produces good accuracy for COVID detection based on chest X-Ray images, in this
study we propose different transfer learning architectures VGG19, MobileNetV2, InceptionResNetV2 and ResNet
(ResNet101V2, ResNet152V2 and ResNet50V2) to analyze their performance, testing conducted in the Google Colab work
environment as a platform for creating Python-based applications and all datasets are stored on the Google Drive application,
the preprocessing stages are carried out before training and testing, the datasets are grouped into theNormal and COVID
folders then combined m become a set of data by dividing them into training sets of 352 images, testing 110 images and
validating 88 images, then the detection results are labeled with the number 1 means COVID and the number 0 for NORMAL.
Based on the test results, the ResNet50V2 model has a better accuracy rate than other models with an accuracy level of about
0.95 (95%) Precision 0.96, Recall 0.973, F1-Score 0.966, and Support of 74, then InceptionResNetV2, VGG19, and
MobileNetV2, so that ResNet50V2-based CNNs can be used as initial identification for the classification of a patientinfected
with COVID or NORMAL.

Mawaddah Harahap*,
2Em Manuel Laia, 3Lilis Suryani Sitanggang, 4Melda Sinaga, 5Daniel Franci Sihombing,
6Amir Mahmud Husein

, Universitas Prima Indonesia

27 februari 2022

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