Towards Precision Pneumonia Diagnosis: A Hybrid Deep Learning Approach Integrating ResNet50 and Vision Transformer

Pneumonia remains one of the leading killers in the world, and thus its early diagnosis is critical to successful treatment. Chest X-ray is a valuable diagnostic imaging modality, but its manual interpretation by radiologists is both time-consuming and prone to mistakes. This paper proposes a hybrid deep learning model for classifying pneumonia vs normal lung using chest X-ray images. The model here integrates the feature extraction capability of ResNet50 with Vision Transformer (ViT) self-attention for better classification accuracy. By utilizing the strengths of these architectures combined, the model achieves satisfactory accuracy in classifying pneumonia-affected and normal lung images. The approach is evaluated on an open chest X-ray dataset and records 96.56% validation accuracy and 86.54% test accuracy. This hybrid model has tremendous potential in automating pneumonia identification, saving diagnostic time, and helping medical practitioners achieve better patient outcomes. The model can be optimized further and the dataset increased for even greater performance in future studies.