Lung tumor segmentation: a review of the state of the art

Abstract

Lung cancer is the leading cause of cancer deaths worldwide. It is a type of cancer that commonly remains undetected due to unpresented symptoms until it has progressed to later stages which motivates the requirement for accurate methods of early detection of lung nodules. Computer-aided diagnosis systems have adapted to aid in detecting and segmenting lung cancer, which can increase a patient's chance of survival. Automatic lung cancer detection and segmentation is a challenging task in aspects of segmentation accuracy. This study provides a comprehensive review of current methods and popular techniques which will aid in further research in lung tumor detection and segmentation. This study presents methods and techniques implemented to solve the challenges associated with lung cancer detection and segmentation and compares the approaches with each other. The methods used to evaluate these techniques and the accuracy rates are also discussed and compared to give insight for future research. Although several combination methods have been proposed over the past decade, an effective and efficient model still needs to be improvised for routine use.