Identification of lung cancer using gray level co-occurrence matrix (GLCM) and artificial neural network with backpropagation algorithm
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Abstract
Air pollution is a problem that occurs in various countries, including Indonesia. One of the consequences of poor air quality due to air pollution is health problems in the lungs, one of which is lung cancer. According to WHO data, lung cancer caused 1.80 million deaths in 2020. This is due to limited services to identify lung cancer early, resulting in delays in treatment. This study aims to identify lung cancer using CT-Scan image processing. The identification method uses a Backpropagation Artificial Neural Network (ANN BP) with Gray Level Co-occurrence Matrix (GLCM) feature extraction. Preprocessing is carried out to improve image quality by removing noise using a median filter. Segmentation of preprocessing results using Otsu threshold. Texture features from segmentation can be calculated from the resulting GLCM, such as Angular Second Moment (ASM)/energy, contrast, correlation, Inverse Different Moment (IDM)/homogeneity, and entropy. These values are obtained from angles of 0°, 45°, 90°, and 135°, and a distance between pixels of 2 pixels. Identification using ANN with Backpropagation algorithm. This study used images of normal lungs and lung cancer with 160 training data images and 40 test data images. The best test results were obtained with the best accuracy level of 92.5%.
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