Abstract
The purpose of this study is to develop a CNN based deep learning model that can effectively detect pneumonia by analyzing chest X-ray images of adults over the age of 20 and compare it with VUNO, LUNIT a commercialized AI model. The data of chest X-ray image was evaluate based on accuracy, precision, recall, F1 score, and AUC score. The CNN model recored an accuracy of 82%, precision 76%, recall 99%, F1 score 86%, and AUC score 0.7937. The VUNO model recordded an accuracy of 84%, precision 81%, recall 94%, F1 score 87%, and AUC score 0.8233. The LUNIT model recorded an accuracy of 77%, precision 72%, recall 96%, F1 score 83%, and AUC score 0.7436. As a result of the Confusion Matrix analysis, the CNN model showe FN (3), showing the highest recall rate (99%) in the diagnosis of pneumonia. The VUNO model showed excellent overall perfomance with high accuracy (84%) and AUC score (0.8233), and the LUNIT model showed high recall rate (96%) but the accuracy and precision showed relatively low results. This study will be able to provide basic data useful for the development of a pneumonia diagnosis system by comprehensively considers the perfomance of the medel is necessary to effectively discriminate between penumonia and normal groups.