Detection and Prevention of Lung Diseases Using Convolutional Neural Networks: A Comprehensive Review

Abstract: Respiratory diseases such as pneumonia, tuberculosis, chronic obstructive pulmonary disease, and lung cancer continue to pose a major threat to global public health. Accurate and early diagnosis remains a critical challenge due to inter-observer variability, radiologist fatigue, and limited access to expert interpretation, particularly in resource- constrained settings. Recent advances in deep learning, specifically convolutional neural networks (CNNs), have enabled automated analysis of chest radiographs and computed tomography scans with accuracy approaching and, in some cases, exceeding expert-level performance. This paper presents a detailed review of CNN-based methodologies for lung dis- ease detection and prevention. The review synthesizes theoretical foundations, learning paradigms, benchmark datasets, state-of-the-art architectures, performance metrics, and prevention-oriented applications such as risk stratification and opportunistic screening. Key challenges related to data bias, explainability, regulatory compliance, and real-world deployment are discussed, along with future research directions toward robust and ethically deployable AI-driven diagnostic systems.

Keywords: Convolutional Neural Networks, Lung Disease Detection, Chest X-ray, Deep Learning, Medical Imaging, Preventive Healthcare