Classification of Malignancy of Lung Cancer Using Backpropagation Algorithm on CT-Scan Images

Abstract

In this study, we investigate the classification of lung cancer CT scan images based on malignancy level using a backpropagation artificial neural network (ANN). Lung cancer is a deadly disease characterized by the growth of abnormal lung cells. The proposed method involves preprocessing to enhance image quality, followed by feature extraction using the Gray Level Co-occurrence Matrix (GLCM) method with angle variations of 0°, 45°, 90°, 135°, and d=1. The extracted features include energy, contrast, correlation, and homogeneity. The energy value range in malignant cancer is 0.27 to 0.81, while in benign cancer it is 0.26 to 0.73. The contrast in benign cancer ranges from 1.38 to 11.87, while in malignant cancer it is 1.47 to 13.67. The image correlation for malignant cancer is between 0.63 to 0.94, while for benign cancer it is 0.69 to 0.96. Homogeneity in malignant cancer has a value range between 0.67 to 0.91, while in benign cancer it ranges from 0.70 to 0.92. The classification of lung cancer malignancy is restricted to benign and malignant levels using a network architecture of [4 10 2], maximum iteration of 100000, and learning rate of 0.001. The accuracy of the testing data from the ANN is between 90% and 100%. These results demonstrate the effectiveness of the GLCM method and backpropagation algorithm in accurately classifying the malignancy level of lung cancer, which could aid in the early detection and treatment of the disease.