KLASIFIKASI MASSA PADA CITRA MAMMOGRAM BERDASARKAN GRAY LEVEL COOCCURENCE MATRIX (GLCM); CLASSIFICATION OF MASSES IN MAMMOGRAM BASED ON GRAY LEVEL COOCCURENCE MATRIX (GLCM)

REFTA LISTIA, Agus Harjoko

2013 | Disertasi | PROGRAM STUDI S2 ILMU KOMPUTER

Breast cancer is the most common disease in women in many countries. Breast cancer can be performed using mammography. In this work, an approach is proposed to classify mammogram based on three classes such as normal, benign, and malignant. The proposed system consist of four major steps : preprocessing, segmentation, feature extraction and classification. In preprocessing grayscale, interpolation, amoeba mean filter and segmentation are applicated. Feature extraction using Gray level Cooccurence Matrix (GLCM) and the features will be calculated in 4 angles (d=1 and d= 2), GLCM 8 angles and GLCM 16 angles. The 5 features are contrast, energy, entropy, correlation and homogeneity. The final step is classification using Backpropagation. Some of important parameters will be variated in this process such as learning rate and the number of node in hidden layer. The research result suggest that extraction feature in 4 angles (0°, 45°, 90°, 135°) and d=1 is the best accuracy for classifying mammogram based on classes 81,1% and especially in 0° accuracy is 100%.

Kata Kunci : Mammogram; GLCM; Backpropagation