DETECTION OF LUNG CANCER USING IMAGE PROCESSING

Abstract

This project constructs and assesses an image processing approach for lung cancer diagnosis in this study. Image processing techniques are frequently utilized for picture improvement in the detection phase to enable early medical therapy in a variety of medical issues. We suggested a lung cancer detection approach based on picture segmentation in this study. Image segmentation is a level of image processing that is intermediate. To segment a CT scan image, a marker control watershed and region growth technique is applied. Following the detection phases, picture augmentation with the Gabor filter, image segmentation, and feature extraction is performed. We discovered the efficiency of our strategy based on the experimental results. The results demonstrate that the watershed with the masking method, which has great accuracy and robustness, is the best strategy for detecting major features. Keywords: Lung cancer, MATLAB, CT images, Distortion removal, Segmentation, Mortality rate.