An appraisal of nodules detection techniques for lung cancer in CT images

Lung cancer has a five-year survival rate of 17.7% which increases to 54.4% when it is diagnosed at early stages. Automated detection techniques have been developed to detect and diagnose nodules at early stages in computer tomography (CT) images. This paper presents a systematic analysis of the recent nodules detection techniques with the goal to summerize current trends and future challenges. The relevant papers are selected from IEEEXplore, science direct, PubMed, and web of science databases. Each paper is critically reviewed in order to summarize its methodology and results for further analysis. Our analyses reveal that several methods show potential progress in the field but still require an improvement to overcome many challenges like, high sensitivity with low false positive (FP) rate, detection of different nodules based on their size, shape, and positions, integration with electronic medical record (EMR) and picture archiving and communication system (PACS), and providing robust techniques that are successful across different databases. To overcome these challenges and developing a robust computer aided detection (CADe) system, it is believed that collaborative work is required among the developers, clinicians and other relating parties in order to understand particular issues and needs of a CADe system and develop automatic techniques to overcome these challenges with high processing speed, low cost of implementation and with software security assurance.