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Enhancement and Segmentation Methods for Lung Cancer Detection System: A Review of A Retrospective Study

Approximation of increased lung cancer cases have been prepared by various research groups, and after perusal of their studies, some basic facts come across. For instance, the research and development for cancer detection and diagnosis systems are increasing rapidly, but still, the rate of occurrence of new cancer cases are escalating. The notion of this article is to brief various potential efforts done towards lung cancer detection and thoracic CT (computed tomography) image analysis. This research has been done by considering studies from the top journal articles of the past two decades. In this paper, significant studies have been framed in a systematic manner to make their comprehension easier and accessible. This review provides an overview of core procedures associated with recent lung-nodule segmentation and discusses opportunistic extents to implement new reliable systems by solving the drawbacks of existing techniques. There are a number of studies, promising the development of efficient algorithm with more than 98% of accuracy, but in order to reach out upon any conclusion, it is necessary to go through various parameters. The most important among all is, size and type of data utilized. Another important parameter is the insertion of algorithms, during varied data processing stages. Apart from this, dimensionality and source of data collection also affect the entire procedure. This article delineates different studies, in such a way, so that, deviation of efficiency due to change of parameter could be easily understood.