AI FOR EARLY DETECTION OF LUNG DISEASES USING CT AND X-RAY IMAGING

Abstract

Early detection of lung diseases, including pneumonia, tuberculosis, chronic obstructive pulmonary disease (COPD), and lung cancer, is critical for effective treatment and improving patient outcomes. Computed tomography (CT) and chest X-ray imaging are standard diagnostic tools, but manual interpretation is time-consuming, subjective, and prone to human error. Artificial intelligence (AI) and deep learning methods, particularly convolutional neural networks (CNNs), provide automated, accurate, and rapid analysis of thoracic imaging, enabling early detection and classification of pulmonary abnormalities. This paper reviews current AI-based approaches for lung disease detection using CT and X-ray imaging, discusses challenges such as image variability, limited annotated datasets, and model interpretability, and highlights the potential of AI systems to enhance diagnostic accuracy, optimize clinical workflows, and improve patient care.