markhsia / pneumonia-ai

A Deep Learning approach to classify lung X-rays to normal or Pneumonia affected lungs.

Pneumonia-AI

A Deep Learning approach to classify lung X-rays to normal or Pneumonia affected lungs.

Dataset

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).

Network Architecture

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.

Training

• 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.

To-do List

• Build a model and train it
• Evaluate the model
• Building a landing page using Flask
• Deploy the app