TY  - JOUR
AU  - Fardela, Ramacos 
AU  - Milvita, Dian 
AU  - Almuhayar, Mawanda 
AU  - Mardiansyah, Dedi 
AU  - Rasyada, Latifah Aulia 
AU  - Hakim, Lukman Mul 
PY  - 2024
TI  - Classification of Thoracic X-Ray Images of COVID-19 Patients Using the Convolutional Neutral Network (CNN) Method
JF  - Journal of Computer Science
VL  - 20
IS  - 4
DO  - 10.3844/jcssp.2024.357.364
UR  - https://thescipub.com/abstract/jcssp.2024.357.364
AB  - Recently, radiology modalities have been widely used to detect COVID-19. Thoracic X-rays and CT scans are the primary radiological tools utilized in the diagnosis and treatment of individuals with COVID-19. In addition, chest CT scans are more accurate and sensitive in early COVID-19 identification. A new problem arises in diagnosing the results of CT scan images of COVID-19 by radiologists or radiology specialists where COVID-19 is difficult to distinguish from pneumonia caused by other viruses and bacteria, so misdiagnosis can occur. Many researchers worldwide have developed computer-aided detection or diagnosis schemes based on medical image processing and machine learning to overcome this challenge. This research focuses on the development of previous studies, where the use of the Convolutional Neural Network (CNN) method to classify Thoracic X-ray Images of COVID-19 Patients is compared with the model developed by Roboflow. Image manipulation techniques applied to this study are pseudo color and the program is Python. This study employs the pseudo color image manipulation technique of the program in Python. This study uses data on patients with confirmed COVID-19 at Andalas University Hospital in 2022. Based on the study's results, a very good CNN Specificity score of 93% was obtained and the perfect Sensitivity score value was produced by the detection method using the Roboflow model, which was 100%. However, the Kappa score for both methods is below the expected threshold of 36-38%. Based on the ROC value, the CNN and Roboflow methods are good for calculating chest X-ray images of COVID-19 and normal patients.