Automated Classification Between Viral and Bacterial Pneumonia Using Convolutional Neural Network (CNN)

Abstract

Pneumonia, one of the most common causes of morbidity and mortality globally, can be caused by either bacterial or viral infection. With chest radiographs as the imaging standard, the timely detection and diagnosis of the disease by experienced radiologists and staff is necessary, especially in less-developed areas. Thus, this study aimed to present an AI-based diagnostic tool that used the best optimizer and hyperparameter values to identify and classify pneumonia from chest X-rays (CXR). Specifically, the study aimed to identify the best AI-based diagnostic by evaluating its performance in three (3) cases: between normal CXRs and pneumonia CXRs; between viral pneumonia CXRs and bacterial pneumonia CXRs; and between normal CXRs, viral pneumonia CXRs, and bacterial pneumonia CXRs. 1000 to 1500 CXR images were used in training the AlexNet CNN model. In addition, different optimizers were evaluated across the three classification cases to determine the model with the best-fit optimizer, which was the RMSprop optimizer. After which, hyperparameter tuning was performed to further optimize the model with image size = 150x150, dropout = 0.4, and batch size = 32. The model was able to achieve an accuracy of 96.5% for classification case A, 81.5% for classification case B, and 85.33% for classification case C. In conclusion, the model was able to identify and classify pneumonia from CXRs in all three cases. Therefore, the proposed model can be used as a supplementary diagnostic tool for pneumonia detection and classification from CXRs.