Deep Learning Techniques for Lung Cancer Detection: A Systematic Literature Review

Lung cancer has been a leading cause of cancer-related deaths, with the number of fatalities in the United Kingdom between 2017 and 2019 reaching 34771, as reported by Cancer Research UK. Lung cancer is when cells inside the lung grow uncontrollably. Detecting nodules in lung cancer at an early stage can increase the chances of survival for humans. Researchers have been investigating the potential of artificial intelligence and deep learning to develop computer-aided detection (CAD) systems for automated lung cancer detection and classification. CAD systems could help radiologists detect lung cancer and improve lung cancer diagnosis accuracy. Our systematic literature review provided an overview of the performance of current deep-learning methods and datasets for detecting and classifying lung cancer using CT images. We conducted a systematic literature review using the PRISMA 2020. This paper gives the reader insights into various facets of lung cancer detection and motivates researchers to further explore opportunities for crafting models that can be seamlessly integrated into a CAD system.