Chest X-ray Image Classification for COVID-19 diagnoses

• Published in: Journal of information systems engineering and business intelligence Vol. 8; no. 2; pp. 109 - 118
• Main Authors: Yuliawan, Endra, ‘Uyun, Shofwatul
• Format: Journal Article
• Language: English
• Published: Universitas Airlangga 29.10.2022
• ISSN: 2598-6333; 2443-2555
• DOI: 10.20473/jisebi.8.2.109-118

Background: Radiologists used chest radiographs to detect coronavirus disease 2019 (COVID-19) in patients and determine the severity levels. The COVID-19 cases were grouped into five classes, each receiving different treatments. An intelligent system is needed to advance the detection and identify vector features of X-ray images with a quality that is too poor to be read by radiologists. Deep learning is an intelligent system that can be used in this case. Objective: The current study compares the classification and accuracy of detection methods with two, three dan five classes. Methods: Deep learning can classify visual geometry group VGG 19 architectures with 1000 classes. The classification of the five classes' convolutional neural network (CNN) underwent model validation with a confusion matrix to produce accuracy and class values. The system could then diagnose patients’ examinations by radiology specialists. Results: The results of the five-class method showed 98% accuracy, the three-class method showed 99.99%, and the two-class showed 99.99%. Conclusion: It can be concluded that using the VGG 19 model is effective. This system can classify and diagnose viruses in patients to assist radiologists by reading the images.   Keywords: COVID-19, CNN, Classification, Deep Learning