Investigating Feature Extraction and Classification Algorithms for Effective Lung Disease Detection Using Chest X-Ray Images

Machine learning is crucial for analyzing data, particularly in tasks like image classification. The COVID-19 pandemic caused a significant rise in lung infections globally, profoundly affecting daily life. Swift identification of infected individuals, including COVID-19 patients, is essential. Researchers often employ machine learning to predict lung disease outcomes by analyzing chest X-ray images. This study used three feature extraction methods such as Pixel, LBP, HOG, and seven classification methods to predict lung disease using X-ray images. SVM showed the highest performance in pixel-based feature extraction. Random forest and XGBoost performed well with LBP extraction and XGBoost also performed better with HOG. These findings highlight the importance of machine learning in healthcare, particularly in quickly identifying lung diseases from X-ray images, a critical tool in managing pandemics like COVID-19.