Lung Cancer Detection Based On CT-Scan Images With Detection Features Using Gray Level Co-Occurrence Matrix (GLCM) and Support Vector Machine (SVM) Methods

Lung cancer is all malignant diseases in the lungs, including malignancies originating from the lungs themselves (primary) or those originating from other organs (metastasis). Lung cancer is one of the leading causes of death worldwide. Lung cancer is a tumor that grows rapidly and can spread to other organs. The onset of cancer is characterized by abnormal cell growth that can damage other normal tissue cells. 

Computerized Tomography (CT) is an imaging technique often used to diagnose lung cancer. Lung cancer can be classified into benign and malignant cancer. It is very important to diagnose lung cancer at an early stage to speed up the treatment process and the actions that will be taken. This study aims to develop a lung cancer detection system based on CT-scan images. 

This detection system has 4 main stages, namely pre-processing of CT-Scan images to improve image quality, segmentation to identify and separate the desired cancer object from the background, feature extraction based on area, contrast, energy, entropy, and homogeneity. The classification of lung cancer into cancer benign and malignant cancer. From the system trial, the accuracy level based on the system decision in determining the diagnosis of lung cancer is benign or malignant.

Keywords: CT-Scan Image, Classification, Lung Cancer, Segmentation, System Detection, Introduction.