Pneumonia Detection

Chest X-Ray Image Classification

This repository contains code for a chest X-ray image classification project. The goal is to classify X-ray images into two classes: "NORMAL" and "PNEUMONIA". The project involves data preprocessing, unsupervised learning using KMeans and GMM, and supervised learning using Convolutional Neural Networks (CNNs).

Below are the steps to set up the project and run the code.

Library Installation

Make sure to install the required libraries by running the following commands:

pip install torch torchvision opencv-contrib-python
pip install scikit-learn kaggle 

Downloading the Data

Before running the code, download the chest X-ray dataset from Kaggle. Ensure you have a Kaggle account and have obtained the API key. Move the kaggle.json file to the required directory:

mkdir -p \~/.kaggle cp ./kaggle.json \~/.kaggle/ chmod 600
\~/.kaggle/kaggle.json 

Download and unzip the dataset:

kaggle datasets download -d paultimothymooney/chest-xray-pneumonia 
unzip chest-xray-pneumonia.zip 

Here are two samples of the Pnemonia and normal images:

Data Preprocessing

The code includes data loading, histogram equalization resizing, and normalization. The dataset is split into training, testing, and validation sets.

Visualization

Explore the dataset through visualization, including randomly selected X-ray images and checking for class imbalance. Here are the train and test class imbalance visualization:

Data Augmentation

To address class imbalance, data augmentation techniques are applied, including random resizing, flipping, Gaussian blur, and affine transformations.

Unsupervised Learning

Apply unsupervised learning techniques such as KMeans and Gaussian Mixture Models (GMM) for feature extraction and clustering. Principle Component Analysis (PCA) is used to reduce the overhead for both methods.

Supervised Learning

Train a Convolutional Neural Network (CNN) for image classification. Two architectures are implemented: a custom CNN and a fine-tuned MobileNetV3.

Model Evaluation

Evaluate the models using metrics such as accuracy, precision, recall, and F1-score. Confusion matrices and ROC curves are also plotted for further analysis.

Comparison

We compared the performance of unsupervised learning (KMeans and GMM) with supervised learning (CNN) and discussed their strengths and weaknesses.