Pneumonia Detection Using Hybrid CNN Model

This medical research introduces a hybrid model using transfer learning from DenseNet201, InceptionResNetV2, and ResNet50 for superior pneumonia detection. Emphasizing the crucial value of early identification, we combine features from these architectures to enhance accuracy. Rigorous experiments on a sizable chest X-ray dataset showcase our model's superiority, achieving 95.67% accuracy, surpassing individual architectures and conventional methods. The model's improved generalization to new cases is attributed to the synergy of transfer learning and diverse architectures. Furthermore, we explore the interpretability of predictions for potential clinical insights. By leveraging these advanced techniques, our project contributes to more effective early-stage pneumonia detection, promising better health care outcomes.