Pneumonia Detection from Chest X-Ray Using Transfer Learning

Abstract: Pneumonia is a severe respiratory disease that requires timely and accurate diagnosis to prevent complications. Traditional diagnostic technique depends on skilled radiologists, which can be difficult and prone to mistakes. In this work, we provide a pretrained ResNet-50 model and transfer learning for an automated pneumonia diagnosis system. The approach classifies instances as normal or pneumonia-positive based on chest x-ray images. To improve pneumonia-specific feature extraction, the transfer method refines the last 20 layers of ResNet-50, which was first trained on ImageNet, using pre-extracted features, To provide better model generalization, the dataset is pre-processed using image augmentation techniques. The model is trained using binarycross-entropy loss and the Adam optimizer, and it achieves 95.35% accuracy on validation data.The trained model is deployed in real time using Gradio on Hugging Faces,resulting in an intuitive user interface. The suggested approach improves the accuracy and efficiency of pneumonia identification, highlighting its potential as a computer-aided diagnostic (CAD) tool in medical imaging