Pneumonia is a lung disease that can be fatal if not accurately diagnosed. The use of Convolutional Neural Networks (CNN) for medical image classification offers a promising approach to aid in diagnosis. This literature review evaluates recent studies focused on CNN-based detection of pneumonia in medical images. Through an examination of various CNN architectures and data processing techniques, this research identifies the advantages of CNN models over conventional methods, as well as challenges such as limited data availability and difficulties in interpreting predictive outcomes. This review concludes that CNN applications for pneumonia classification show significant potential, although further optimization is required, particularly in improving model generalization across diverse datasets. This study aims to serve as a reference for the development of artificial intelligence-based diagnostic technology in the medical field.
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