Lung cancer is one of the dangerous and life taking disease in the world. However, early diagnosis and treatment can save our life. Although, CT scan imaging is best imaging technique in medical field, it is difficult for doctors to interpret and identify the cancer from CT scan images. Therefore computer aided diagnosis(CAD) can be helpful for doctors to identify the cancerous cells accurately. Many computer aided model using image processing and Machine Learning Technique(MLT) has been researched and developed. The main goal of this research work is to evaluate the various computer-aided model, analyzing the current best model and finding out their limitation and drawbacks and finally proposing the new model with improvements in the current best model. The model utilized that lung cancer detection model were sorted and arranged on the basis of their detection accuracy. The model were developed on each step and overall limitation, drawbacks were pointed out. It is found that some has low accuracy and some has higher accuracy, but not nearer to 100%. Therefore, this research targets to increase the accuracy towards 100%.

Image Processing, Data Mining, Segmentation, Classification, Lung Cancer, Prediction

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