A deep learning or transfer learning project to classify chest X-rays of pediatric patients as either showing signs of pneumonia or not.
About the project:
This is a deep learning project that aims to develop an accurate and efficient model for classifying chest X-rays of pediatric patients as either showing signs of pneumonia or not. The project involves using transfer learning, which involves taking a pre-trained deep learning model(DenseNet-161) that has already been trained on a large dataset and retraining it on the pneumonia dataset. The model architecture typically involves a convolutional neural network (CNN) that has been modified to suit the needs of the pneumonia classification task. The model is trained using a dataset of labeled chest X-rays and is then tested on a separate set of validation and testing data. The ultimate goal of the project is to deploy the model in a production environment where it can be used to help diagnose pneumonia in pediatric patients, potentially leading to earlier detection and better treatment outcomes.
Link for the dataset used : https://www.kaggle.com/datasets/andrewmvd/pediatric-pneumonia-chest-xray