COVID-19 Lung X-Ray Image Classification

This project focuses on classifying X-ray images of lungs, distinguishing between COVID-19 infected and normal cases using traditional machine learning techniques. The process includes:

Dataset: X-ray images sourced from Kaggle. Preprocessing: Includes Gaussian filtering, contrast enhancement, and segmentation to improve image quality. Feature Extraction: Utilizes Histogram of Oriented Gradients (HOG) and color histograms. Classification: Implements Naive Bayes, Decision Tree, and K-Nearest Neighbors (KNN) classifiers. Evaluation: Metrics like confusion matrix, accuracy, precision, recall, and F1-score were used to evaluate model performance. The study highlights the effectiveness of preprocessing techniques and compares the performance of different classifiers, with KNN achieving the best results.