Lung Cancer Detection and Prediction of Cancer Stages Using Image Processing

Abstract

Lung cancer is one of the most common and dangerous cancers in the world. However, lives can be saved through early diagnosis by CT scan images, which is the best imaging technique in the medical field for early treatment. Though CT scan imaging is the best technique, doctors and radiologists face some difficulties such as not being able to diagnose early and commence treatment and to interpret and identify cancer from CT scan images because of the limitation of equipment and specialists. Therefore, to identify the cancerous cells accurately, computer-aided diagnosis can be more helpful for doctors. Computer-aided techniques based on image processing and machine learning have been extensively researched, and are being implemented currently to address this issue. This research is mainly focused on evaluating and analyzing the different computer-aided techniques, find out their limitations and drawbacks and finally, propose a new model with improvements. In the methodology section of this research, lung cancer detection techniques were sorted and listed based on their detection accuracy. The techniques were analyzed on each step, and overall limitations and disadvantages were pointed out. It is found that some techniques have low accuracy and some have higher accuracy, but not nearer to 100%. Therefore, our project target is to make a lung cancer detection model using CT scan images with high accuracy and predicting the lung cancer stage.