COVID-19 vs Healthy Lung Classification

This repository contains a deep learning model for classifying chest X-ray images as either COVID-19 or Healthy. The model is built using a Convolutional Neural Network (CNN) with Keras.

Overview

The objective of this project is to train a CNN model to distinguish between COVID-19 infected lungs and healthy lungs based on X-ray images. The model is trained using images that are augmented to improve its generalization capabilities.

Features

• Image Preprocessing:

  • Images are rescaled, rotated, zoomed, and flipped using Keras' ImageDataGenerator to create a more diverse training dataset and help prevent overfitting.

• CNN Architecture:

  • The model consists of Conv2D layers for feature extraction, MaxPooling2D layers for down-sampling, and Dense layers for classification.

• Optimization:

  • The model uses the Adam optimizer and binary cross-entropy loss function.
  • Early stopping is applied to prevent overfitting, saving the best model based on validation loss.

• Metrics:

  • The model's performance is monitored using accuracy and loss curves for both training and validation sets.
  • After training, the model's performance is evaluated using a confusion matrix and classification report.

Dataset

The dataset is divided into three directories:

• TRAIN/: Contains the training images for both COVID-19 and Healthy classes.
• VAL/: Contains the validation images for both COVID-19 and Healthy classes.
• TEST/: Contains the test images (used for final evaluation).

Dataset Structure: