Open AccessDiagnosa COVID-19 Chest X-Ray Menggunakan Arsitektur Inception Resnet
Author(s) -
Adhitio Satyo Bayangkari Karno Satyo,
Dodi Arif,
Indra Sari Kusuma Wardhana,
Eka Sally Moreta
Publication year - 2021
Publication title -
journal of informatics and information security
Language(s) - English
Resource type - Journals
ISSN - 2722-4058
DOI - 10.31599/jiforty.v2i1.646
Subject(s) - covid-19 , convolutional neural network , pneumonia , residual neural network , computer science , artificial intelligence , deep learning , pattern recognition (psychology) , medicine , pathology , disease , infectious disease (medical specialty)
The availability of medical aids in adequate quantities is very much needed to assist the work of the medical staff in dealing with the very large number of Covid patients. Artificial Intelligence (AI) with the Deep Learning (DL) method, especially the Convolution Neural Network (CNN), is able to diagnose Chest X-ray images generated by the Computer Tomography Scanner (C.T. Scan) against certain diseases (Covid). Inception Resnet Version 2 architecture was used in this study to train a dataset of 4000 images, consisting of 4 classifications namely covid, normal, lung opacity and viral pneumonia with 1,000 images each. The results of the study with 50 epoch training obtained very good values for the accuracy of training and validation of 95.5% and 91.8%, respectively. The test with 4000 image dataset obtained 98% accuracy testing, with the precision of each class being Covid (99%), Lung_Opacity (97%), Normal (99%) and Viral pneumonia (99%).