AUTOMATED DETECTION OF PULMONARY DISEASES USING CT AND MRI WITH DEEP LEARNING

Abstract

Pulmonary diseases, including pneumonia, chronic obstructive pulmonary disease (COPD), and lung cancer, are major contributors to global morbidity and mortality. Early and accurate diagnosis is critical for effective treatment and improved patient outcomes. Traditional diagnostic methods rely on radiologist interpretation of CT and MRI images, which can be time-consuming and subject to inter-observer variability. Artificial intelligence (AI) and deep learning techniques offer automated, precise, and efficient solutions for pulmonary disease detection. This paper reviews current AI-based methodologies for automated detection of pulmonary diseases using CT and MRI scans, with a focus on convolutional neural networks (CNNs), segmentation models, and hybrid approaches. Challenges such as image variability, dataset limitations, and model interpretability are discussed. The study emphasizes the potential of AI systems to enhance diagnostic accuracy, optimize clinical workflow, and improve patient care in pulmonary medicine.