Gray Level Co-Occurrence Matrices and Support Vector Machine for Improved Lung Cancer Detection

Abstract

A detection system based on digital image processing and machine learning classification was developed to detect normal and cancerous lung conditions. 340 data from The Lung Image Database Consortium and Image Database Resource Initiative (LIDC–IDRI) were processed through several stages. The first stage is pre-processing using three filter variations and contrast stretching, which reduce noise and increase image contrast. The image segmentation process uses Otsu Thresholding to clarify the Region of Interest (ROI) of the image. The texture feature extraction with Gray Level Co-Occurrence Matrices (GLCM) was applied using 21 feature variations. Data extraction is used as a label value learned by the classification system in the form of Support Vector Machine (SVM). The results of the training data classification are processed with a confusion matrix which shows that the high pass filter has higher accuracy than the other two variations. The proposed method was assessed in terms of accuracy, precision and recall. The model provided an accuracy of 99.33 % training data and 97.50 % testing data