This project aims to build a complete pattern recognition system to solve classification problems using the k-Nearest Neighbors (KNN) algorithm. The system is designed to classify chest X-ray images into three categories: COVID-19 positive, pneumonia positive, and normal. To achieve this, we utilize the COVID-19 Chest X-ray dataset available on Kaggle.
The dataset used in this project is the COVID-19 Chest X-ray dataset which consists of chest X-ray images labeled as COVID-19 positive, pneumonia positive, and normal.
- K-Nearest Neighbors (KNN) Algorithm: KNN is a simple and effective algorithm used for classification problems.
- Centroids for Feature Exteraction: centroids is used to extract features from data
- Swarm Optimization (Ant Colony) for Feature Selection: Swarm optimization is utilized for feature selection, helping in identifying the most relevant features for classification.
- Linear Discriminant Analysis (LDA) for Feature Reduction: LDA is used to reduce the dimensionality of the feature space while preserving the class discriminatory information.
- Make sure to download the dataset from Kaggle and place it in the
data/directory before running the code.
Feel free to modify and improve this project. If you have any questions or suggestions, please feel free to open an issue or contact me directly.