Evaluation on Lung Cancer Detection Using Computer Assisted Diagnosis (CAD) System

Lung cancer considered to be the worst threat to humankind, and it is also painstaking to be the main cause of death among other death casualties of cancer. This paper will review the detection techniques of lung cancer by using data science technology. Computer-assisted diagnosis CAD system is very effective in the detection of lung cancer and it is of high importance as it can increase the survival of the patient. Mainly there are four main stages in the CAD. First is the image preprocessing, Segmentation and ROI selection of lung portions, next is nodule detection inside the lung fields, and then comes the classification of that nodule and then evaluation of the nodules. Early detection of this disease can increase the survival chances of the patient because the time factor plays a succor role in this regard. Image Processing technique helps in getting the exact core image of the defected area which can be treated in time. The image processing system must be accurate and clear with a good pixel percentage and labeling of the mask. Pulmonary diseases can cause serious impact on human body if it is misdiagnosed and improper diagnosis done thru the medical analysis. Medical professionals assess and checks numerous of radiology images (CT Images) and inspects the lung cancer disease from it. However now a days Image processing technique applies with Computer aided system in this regard and becoming paramount aid in the field of medical science. This paper will follow segmentation processes, based on morphology operation there is technique that can diminish misleading results that is known as region of interest (ROI) extraction. The better results possible to achieved with the use of a convolution neural network. Computer aided detection has more advantages in terms of speed and accuracy. Today researchers are trying to improve the working of computer aided systems for better performance, they are also striving hard to make computer aided systems as a best approach for lung cancer detection.