Texture feature extraction for the lung lesion density classification on computed tomography scan image - Hasnely, Hasnely; Nugroho, Hanung Adi; Wibirama, Sunu; Windarta, Budi; Choridah, Lina (Komunitas Ilmuwan dan Profesional Muslim Indonesia, 2016)
The radiology examination by computed tomography (CT) scan is an early detection of lung cancer to minimize the mortality rate. However, the assessment and diagnosis by an expert are subjective depending on the competence and experience of a radiologist. Hence, a digital image processing of CT scan is necessary as a tool to diagnose the lung cancer. This research proposes a morphological characteristics method for detecting lung cancer lesion density by using the histogram and GLCM (Gray Level Co-occurrence Matrices). The most well-known artificial neural network (ANN) architecture that is the multilayers perceptron (MLP), is used in classifying lung cancer lesion density of heterogeneous and homogeneous. Fifty CT scan images of lungs obtained from the Department of Radiology of RSUP Dr. Sardjito Hospital, Yogyakarta are used as the database. The results show that the proposed method achieved the accuracy of 98%, sensitivity of 96%, and specificity of 96%.