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All Journal KERNEL: Jurnal Riset Inovasi Bidang Informatika dan Pendidikan Informatika
Adhitio Bayangkari Satyo Karno
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Diagnosa COVID-19 Chest X-Ray Dengan Convolution Neural Network Arsitektur Resnet-152 Widi Hastomo; Adhitio Bayangkari Satyo Karno
KERNEL: Jurnal Riset Inovasi Bidang Informatika dan Pendidikan Informatika Vol 2, No 1 (2021)
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (812.148 KB) | DOI: 10.31284/j.kernel.2021.v2i1.1884

Abstract

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). Resnet Version-152 architecture was used in this study to train a dataset of 10.300 images, consisting of 4 classifications namely covid, normal, lung opacity with 3,000 images each and viral pneumonia 1,000 images. 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 10.300 image dataset obtained 98% accuracy testing, with the precision of each class being Covid (99%), Lung_Opacity (99%), Normal (98%) and Viral pneumonia (98%). 
Co-Authors Widi Hastomo