I developed a deep learning model to detect pneumonia in X-ray images using computer vision techniques. I preprocessed a large dataset of chest X-ray images and used convolutional neural networks (CNNs) to classify the images as either normal or pneumonia. I implemented the model using a full stack approach, including data preprocessing, model training and testing, and deploying the model as a web application. To improve the accuracy of the model, I explored various CNN architectures such as VGG-16, ResNet, and InceptionV3, and fine-tuned their hyperparameters. I also evaluated the performance of the model using various metrics such as accuracy, precision, recall, and F1-score.Finally, I deployed the model as a web application that allows users to upload a chest X-ray image and receive a prediction of whether they have pneumonia or not. The application also provides visualizations and explanations of the prediction to improve transparency and user understanding. Overall, this project demonstrates my expertise in deep learning, computer vision, and full stack development, and shows how these skills can be applied to a real-world problem in healthcare.