Graphical User Interface based platform for the Lung Cancer Classification

Abstract

Lung cancer has become of the major health issues in recent year. Early detection and remedy is very important to reduce the chances of death of the sufferers. In this work, Gabor filter image processing has been used to reduce the noise in the images received from the data set along with watershed segmentation to define the image. Features such as mean, standard deviation and energy are found in the clusters of the Lung CT images. RMS, skewness, etc. are also attained. A trained model is created using the extracted features and fed to a support vector machine. An accuracy of 94% has been achieved in the classification of early lung cancer detection. A variety of image processing techniques has been employed to detect the pulmonary cellular breakdown. This research will assist the medical practitioner to diagnose lung cancer at early stages in future,