<![CDATA[Pneumonia is a serious respiratory disease that continues to be a major worldwide health issue, especially in nations that are struggling with limited medical resources. Early and accurate detection is essential to improve patient outcomes and reducing the rate of death. This study compares the performance of two deep learning architectures, DenseNet121 and ResNet50, using transfer learning for pneumonia detection from chest X-ray images. The dataset consists 5,856 images with two classes, NORMAL and PNEUMONIA, split into training 60%, validation 20%, and testing 20%. Pretrained ImageNet weights were used as fixed feature extractors, with a custom classification layers. Evaluation metrics included accuracy, precision, recall, F1-score, and confusion matrix. On the internal test set, DenseNet121 achieved 92% accuracy, with precision 0.79, recall 0.94, and F1-score 0.86 for NORMAL class, and precision 0.98, recall 0.91, and F1-score 0.94 for PNEUMONIA class. ResNet50 reached 81% accuracy, with precision 0.61, recall 0.80, and F1-score 0.70 for NORMAL class, and precision 0.92, recall 0.81, and F1-score 0.86 for PNEUMONIA class. External testing on an independent set of 200 images (100 images per class) yielded 98% accuracy for DenseNet121 and 85% for ResNet50. These results show that DenseNet121 provides better overall performance and lower false-negative risk for pneumonia cases, highlight the potential of DenseNet121 as a foundation for AI-assisted diagnostic tools in clinical practice.]]>
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