KIPMIKIPMI

Communications in Science and TechnologyCommunications in Science and Technology

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%.

This research proposes a method to identify the characteristics and classification of lesion density of primary lung cancer by using the histogram and GLCM-based texture feature extraction.The combination of histogram and GLCM-based texture feature extraction achieved an accuracy of 98%, sensitivity of 96%, and specificity of 96%.These results demonstrate the ability of the methods to differentiate lesion density between heterogeneous and homogeneous lung cancer, potentially aiding radiologists in image interpretation and serving as a component in the development of Computer-Aided Diagnosis (CAD) systems for lung cancer.

Penelitian lebih lanjut dapat dilakukan dengan mengeksplorasi teknik segmentasi yang berbeda untuk meningkatkan akurasi deteksi lesi. Selain itu, pengembangan metode ekstraksi fitur baru yang lebih adaptif terhadap variasi karakteristik lesi pada citra CT scan dapat meningkatkan kinerja klasifikasi. Terakhir, studi komparatif dengan metode deep learning, seperti Convolutional Neural Networks (CNN), perlu dilakukan untuk mengevaluasi potensi peningkatan akurasi dan efisiensi dalam diagnosis kanker paru-paru. Penelitian-penelitian ini diharapkan dapat menghasilkan sistem CAD yang lebih andal dan akurat, sehingga membantu dokter dalam membuat keputusan klinis yang lebih tepat dan meningkatkan hasil pengobatan pasien.

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