Lung Cancer Detection in Radiological Imaging using Deep Learning: A Review

Lung cancer remains a leading cause of cancer-related deaths globally, requiring creative methods to improve detection efficiency. Data quantity and quality, algorithm complexity, integration into healthcare workflows, and ethical and legal issues are some of the challenges facing artificial intelligence (AI). The study aims to provide a comprehensive overview of state-of-the-art deep learning techniques, in particular Convolutional Neural Networks (CNNs), for the diagnosis of lung cancer using medical imaging modalities such as Computed Tomography CT scans and X-rays. Some of the primary objectives are to assess algorithm performance, identify gaps and trends, enhance clinical applications, and propose new research directions. To focus future research efforts on more useful solutions, the paper also discusses the need for a larger range of datasets and improved model interpretability. It also considers how deep learning technology could be incorporated into routine screenings to improve patient outcomes and early detection rates. More stable datasets, improved algorithm performance, and the development of morally sound AI deployment frameworks for use in healthcare environments are some of the next directions to pursue. With increasingly powerful diagnostic instruments, notably Computer-Aided Diagnosis (CAD) systems, scholars and practitioners should acquire valuable insights from this investigation, which will ultimately improve patient treatment.