<![CDATA[Lung infections, especially viral pneumonia, continue to pose a significant global health challenge due to their high rates of illness and death. Traditional diagnostic approaches, such as radiologists' interpretation of chest X-ray (CXR) images, are frequently slow and subject to personal bias. The swift advancement in deep learning offers great potential for automating the detection of lung infections; however, many existing convolutional neural network (CNN) models demand substantial computational resources, which restricts their use in real-time or low-resource clinical settings. This study seeks to overcome these issues by creating a lightweight and effective diagnostic system using the MobileNet architecture for automatic lung infection identification from CXR images. The core drive for this research is to deliver an accessible and precise AI tool that aids radiologists in timely disease detection, particularly in under-resourced healthcare environments. The proposed MobileNet-based model, trained through transfer learning and fine-tuning on a binary dataset of normal and viral pneumonia images, strikes an excellent balance between performance and computational efficiency. Experimental results yielded 98% accuracy, 0.98 precision, 0.98 recall, and 0.98 F1-score, validating the model's reliability and appropriateness for embedded or mobile health uses. Moving forward, efforts will concentrate on broadening the dataset to encompass various lung disease types, incorporating explainable AI methods to boost clarity, and implementing the model in live clinical or mobile diagnostic platforms to enable widespread and effective healthcare services.]]>
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