Pneumonia Detection Using Deep Learning Models: A Comparative Study on the RSNA Dataset

Pneumonia Detection Using Deep Learning Models: A Comparative Study on the RSNA Dataset This research paper presents a comparative analysis of deep learning models for pneumonia detection using chest X-rays from the RSNA dataset. The study evaluates DenseNet201, ResNet, Xception, and EfficientNet architectures on metrics such as accuracy, precision, recall, and AUC. DenseNet201 achieved the highest performance with an accuracy of 80% and an AUC of 0.85. The methodology includes detailed preprocessing, data augmentation, class imbalance handling, and model fine-tuning. This work demonstrates the potential of AI in assisting early diagnosis of pneumonia and improving clinical workflows.