A Deep Learning approach to classify lung X-rays to normal or Pneumonia affected lungs.
The data has been provided by kaggle to help automate the process of detecting diseases from medical images.
- Chest-X-Ray Images - https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia
- The dataset is organized into 3 folders (train, test, val) and contains subfolders for each image category (Pneumonia/Normal). There are 5,863 X-Ray images (JPEG) and 2 categories (Pneumonia/Normal).
Clearly, it is an image classification problem. Hence, a custom CNN network has been used with the following architecture:-
- 3 (3 x 3) 2D Convolutional Layers with relu activation
- Each convolutional layer followed bya (2 x 2) 2D Max Pooling
- Two fully-connected layers with 50% dropout.
- The images are prprocessed and resized to (150 , 150).
- The model is compiled using Adam optimizer , binary crossentropy as loss and accuracy as metrics.
- The model is trained for 20 epochs.
- An accuracy of around 90% is achieved by the model on the test data set.
- Build a model and train it
- Evaluate the model
- Building a landing page using Flask
- Deploy the app