<![CDATA[Pneumonia is a lung infection and one of the leading causes of mortality worldwide. Early and accurate diagnosis is essential to reduce death rates, with chest X-ray (CXR) imaging being the most commonly used diagnostic tool. However, CXR-based pneumonia identification remains challenging due to limited image quality and the shortage of experienced radiologists. To address this issue, this study proposes a hybrid deep learning framework that integrates Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Generative Adversarial Network (GAN) to enhance the classification of bacterial and viral pneumonia from CXR images. The dataset comprises 7,927 CXR images, including 3,270 normal cases, 3,001 cases of bacterial pneumonia, and 1,656 cases of viral pneumonia. Four CNN architectures, Xception, InceptionV3, ResNet50V2, and DenseNet201, are evaluated using RMSprop and Stochastic Gradient Descent (SGD) optimizers. Model development and training are conducted using the TensorFlow framework. Experimental results demonstrate that ResNet50V2 with the RMSprop optimizer achieves the highest classification accuracy of 0.85, while also yielding the fastest training time of 2,215 seconds. These findings indicate that the proposed approach can support faster and more accurate pneumonia screening, particularly in healthcare facilities with limited diagnostic resources]]>
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