Open AccessDeteksi Penyakit Covid-19 Pada Citra X-Ray Dengan Pendekatan Convolutional Neural Network (CNN)
Author(s) -
Mawaddah Harahap,
Em Manuel Laia,
Lilis Suryani Sitanggang,
Melda Sinaga,
Daniel Franci Sihombing,
Amir Mahmud Husein
Publication year - 2022
Publication title -
jurnal resti (rekayasa sistem dan teknologi informasi)
Language(s) - English
Resource type - Journals
ISSN - 2580-0760
DOI - 10.29207/resti.v6i1.3373
Subject(s) - convolutional neural network , artificial intelligence , computer science , covid-19 , preprocessor , python (programming language) , pattern recognition (psychology) , test set , deep learning , transfer of learning , identification (biology) , artificial neural network , medicine , pathology , infectious disease (medical specialty) , botany , disease , biology , operating system
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.