Pengolahan Citra Digital untuk Identifikasi Kanker Otak Menggunakan Metode Deep Belief Network (DBN)

Muhammad Syaifulloh Fattah, Dina Zatusiva Haq, Dian Candra Rini Novitasari

Abstract


The brain tumor is a dangerous disease for humans that can interfere with the functioning of the human brain. Brain tumors can develop into malignant brain tumors or brain cancer and cause death, so early detection is necessary to diagnose brain tumor disease. One way of early detection is to use the anatomy of an MRI scan of health images. The MRI scan results can diagnose patients, but it takes longer time. Therefore digital image processing is needed to facilitate an analysis so that it can be seen in the brain image there are tumor cells or not. In addition to digital image processing, a system that analyzes and detects data is also needed. The Deep Belief Network (DBN) method is used to identify data. This study conducted trials on the learning rate and network architecture. The results of the identification of brain cancer using the DBN method obtained a sensitivity (TP rate) value of 90.9%, a specificity (TN rate) of 100%, an accuracy of 95%, and a precision of 100% with a learning rate of 0.1 and using a 4-12-10-1 network architecture.


Keywords


Brain cancer; MRI identification; Digital Image Processing; DBN

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References


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DOI: http://dx.doi.org/10.32493/informatika.v6i4.13089

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Copyright (c) 2022 Muhammad Syaifulloh Fattah, Dina Zatusiva Haq, Dian Candra Rini Novitasaei

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Jurnal Informatika Universitas Pamulang (ISSN: 2541-1004 e-ISSN: 2622-4615)



This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License