This project is designed to detect pneumonia in chest X-ray images using three pre-trained deep learning models: VGG19, ResNet50, and MobileNet. The models were trained on a labeled dataset containing both normal and pneumonia cases.

To set up the project, follow these steps:

Clone this repository to your local machine using the following command:

git clone https://github.com/s20488/NAI_project_pneumonia_detection.git

Navigate to the project directory and install the required Python libraries:

pip install -r requirements.txt

The dataset was sourced from Kaggle: https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia/data

Ensure the dataset is organized in the following structure:

data/
|-- chest_xray/
|   |-- train/
|       |-- NORMAL/
|       |-- PNEUMONIA/
|   |-- val/
|       |-- NORMAL/
|       |-- PNEUMONIA/
|   |-- test/
|       |-- NORMAL/
|       |-- PNEUMONIA/

Launch the app and visit http://localhost:5000/ in your browser to upload and predict X-ray images.

python main.py

Portions of the code were adapted from this Kaggle notebook: https://www.kaggle.com/code/karan842/pneumonia-detection-transfer-learning-94-acc/notebook#Importing-necessary-libraries.