Pneumonia detection system using convolutional neural network with DenseNet201 architecture - Qomaruddin, Muhammad; Riansyah, Andi; Hermawan, Hildan Mulyo; Taufik, Moch (International Journal of Informatics and Communication Technology (IJ-ICT), 2025)
The diagnosis of pneumonia remains a significant challenge for medical practitioners worldwide, particularly in regions with limited healthcare resources. Traditional interpretation of chest X-rays is time-consuming and often subjective, especially when images are of low quality. This study presents the development of a web-based system utilizing the DenseNet201 architecture to address these challenges. A series of experiments were conducted to evaluate three optimizers Adam, Adamax, and Adadelta over fifty epochs. Among them, Adamax yielded the best performance, achieving a training accuracy of 93.67% and a validation accuracy of 94.20%. When tested on new data, the system consistently delivered high performance, with accuracy, precision, recall, and F1 score all reaching 96%. These results suggest that the proposed system has the potential to significantly enhance the accuracy and efficiency of pneumonia diagnosis based on chest X-rays.