Communications in Science and Technology
Vol 1 No 1 (2016)

Texture feature extraction for the lung lesion density classification on computed tomography scan image

Hasnely Hasnely (Universitas Gadjah Mada)
Hanung Adi Nugroho (Unknown)
Sunu Wibirama (Unknown)
Budi Windarta (Unknown)
Lina Choridah (Unknown)

Article Info

Publish Date
31 May 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%.

Copyrights © 2016

Journal Info







Communication in Science and Technology [p-ISSN 2502-9258 | e-ISSN 2502-9266] is an international open access journal devoted to various disciplines including social science, natural science, medicine, technology and engineering. CST publishes research articles, reviews and letters in all areas of ...