Using Deep Learning to Identify Types of Lung Diseases from X-Ray Images

Lung illness is a major global health issue that affects millions of people and heavily strains healthcare systems across the globe. Effective therapy and positive patient outcomes depend heavily on an early and correct diagnosis. This work offers a theoretical basis for using deep learning methods on X-ray images to identify different kinds of lung illnesses. In recent years, deep learning and Convolutional Neural Networks (CNNs) have become a potent tool for medical image interpretation. In radiography, its capacity to extract complex patterns and representations from data has shown to be revolutionary. The research goal is to develop a reliable and automated system that can identify various lung disorders such as COVID, lung cancer, pneumonia, and tuberculosis by utilizing CNNs on X-ray images. The paradigm incorporates ethical considerations about data protection, fairness, and interpretability to guarantee the proper application of AI in healthcare. Meeting regulatory standards and gaining the trust of patients and healthcare providers both depend on addressing these concerns. The goal of this study is to further the current discussion on deep learning and artificial intelligence’s application to health care, particularly in the area of lung disease diagnostics. Although the main focus of this study is theoretical, it establishes the foundation for prospective practical applications that could transform the diagnosis of lung disorders and ultimately result in earlier therapies, better patient care, and improved global health outcomes.