KLASIFIKASI POLA IMAGE PADA PASIEN TUMOR OTAK BERBASIS JARINGAN SYARAF TIRUAN ( STUDI KASUS PENANGANAN KURATIF PASIEN TUMOR OTAK )

Sri Heranurweni, Budiani Destyningtias, Andi Kurniawan Nugroho

Abstract


 Nowadays medical science has developed rapidly, diagnostic and treatment techniques have provided life expectancy for patients. One way of examining brain tumor sufferers is radiological examination that needs to be done, including MRI with contrast. MRI brain images are useful for seeing tumors in the initial steps of diagnosis and are very good for classification, erosions / destruction lesions of the skull. Smoothing image processing, segmentation with otsu method and feature extraction are carried out to facilitate the training and testing process. This study, will apply texture analysis with the parameters contrast, correlation, energy, homogenity to distinguish the texture of brain tumor images and normal so as to produce a standard gold value based on existing texture characteristics. Training and testing of texture features using backpropagation method of artificial neural networks with variations in learning rate values so that it is expected to obtain a classification of the image conditions of patients with brain tumors. The data used are 29 brain images that produce classification accuracy of 96.55%.

Keywords :  MRI images, brain tumors, textur, backprogation 


Keywords


MRI images, brain tumors, textur, backprogation

Full Text:

PDF

References


(1) Agung Adinegoro, Ratri Dwi Atmaja, Rita Purnamasari, 2105, Deteksi Tumor Otak dengan Ektrasi Ciri & Feature Selection mengunakan Linear Discriminant Analysis (LDA) dan Support Vector Machine (SVM, e-Proceeding of Engineering : Vol.2, No.2 Agustus 2015, Page 2532 ISSN 2355-9365 (2) Modul Hematologi Onkologi, HO15_Tumor-Otak, http://spesialis1.ika.fk.unair.ac.id/download diakses tanggal 12 Juli 2018 (3) Russell, Norvig, 2010, Artificial Intelligence: A Modern Approach, 3rd ed, New Jersey, Prentice Hall (4) Scott W. Atlas, MD, 2009, Magnetic Resonance Imaging of the Brain and Spine Volume Two 4th Edition, Lipincot Williams & Wilkins, California (5) Y. Zhang, L. Wu, and S. Wang, 2011, Magnetic Resonance Brain Image Classification By An Improved Artificial Bee Colony Algorith, Progress In Electromagnetics Research, Vol. 116, 65-79 (6) Y. Zhang, L. Wu, 2012, An Mr Brain Images Classifier Via Principal Component Analysis And Kernel Support Vector Machine, Progress In Electromagnetics Research, Vol. 130, 369-388 (7) Y. Zhang, L. Wu, An Mr Brain Images Classifier Via Principal Component Analysis And Kernel Support Vector Machine, Progress In Electromagnetics Research, Vol. 130, 369-388

(8) Yeni Lestari Nst, Mesran, Suginam, Fadlina, 2017, Sistem Pakar Untuk Mendiagnosis Penyakit Tumor Otak Menggunakan Metode Certainty Factor (CF), Jurnal INFOTEK, Vol 2, No 1, Februari 2017 hal 8286 ISSN 2502-6968 (Media Cetak) (9) Vinny Maritaa, Nurhasanaha, Iklas Sanubarya, tahun 2014 meneliti tentang Identifikasi Tumor Otak Menggunakan Jaringan Syaraf Tiruan Propagasi Balik pada Citra CT-Scan Otak, PRISMA FISIKA, Vol. V, No. 3 (2014), Hal. 117-121, ISSN : 23378204




DOI: http://dx.doi.org/10.26623/elektrika.v10i2.1169

Refbacks

  • There are currently no refbacks.


View My Stats