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Hasnely Hasnely
Universitas Gadjah Mada
Engineering

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Texture feature extraction for the lung lesion density classification on computed tomography scan image Hasnely Hasnely; Hanung Adi Nugroho; Sunu Wibirama; Budi Windarta; Lina Choridah
Communications in Science and Technology Vol 1 No 1 (2016)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.1.1.2016.14

Abstract

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%.
Co-Authors Budi Windarta Hanung Adi Nugroho Lina Choridah Sunu Wibirama