Synthesizing Radiological Insights: Enhancing Lung Disease Classification through Multimodal Imaging

Abstract

Precisely categorizing lung diseases is essential for effective medical treatments. This paper presents a comprehensive analysis of advanced methods in lung disease classification, with a focus on integrating diverse imaging techniques like computerized tomography (CT), X-rays, and magnetic resonance imaging (MRI). These imaging approaches collectively enhance the understanding of pulmonary conditions, aiding in early detection and differential diagnosis. The paper initially explains the fundamental principles of CT, MRI, and X-rays, highlighting their unique characteristics and roles in elucidating lung structures. It explores state-of-the-art methodologies, encompassing both traditional machine learning using engineered features and the expanding domain of deep learning utilizing neural networks to classify intricate diseases. A wide range of prevalent lung ailments, spanning from pneumonia and lung cancer to chronic obstructive pulmonary disease (COPD), are covered. Each domain delves into the considerations for adapting imaging modalities, involving data pre-processing, feature extraction, and algorithmic orchestration. Comparative evaluations of performance metrics offer insights into the effectiveness and limitations of each approach. Furthermore, the paper outlines the challenges associated with classifying lung diseases, including limited annotated data, complexities in model interpretation, and the seamless integration of algorithmic outcomes into clinical practices. As for future research avenues, the paper suggests innovative directions such as data augmentation, integrating multi-modal imaging information, and advancing transparent artificial intelligence (AI) frameworks to enhance their acceptance in clinical settings.