Klasifikasi Tingkat Keparahan Non-ProliferativeI Diabetic Retinopathy Bedarsarkan Hard Exudate Menggunakan Extreme Learning Machine

Dinda Ulima Rizky Yani, Dwi Ratna Sulistyaningrum
Submission Date: 2017-08-03 12:44:12
Accepted Date: 2017-12-31 15:38:17

Abstract


Diabetic Retinopathy dapat menyebabkan seseorang  kehilangan kemampuan penglihatannya dan pada keadaan yang parah dapat mengakibatkan kebutaan. Tingkat keparahan Non-Proliferative Diabetic Retinopathy (NPDR) dapat diketahui dengan mendeteksi kelainan berupa hard exudate pada retina, namun diagnosanya tidak bisa dilakukan dengan cepat karena pengamatan retina harus melewati beberapa proses. Teknologi pengolahan citra digital berbasis machine learning telah banyak digunakan untuk menyelesaikan permasalahan ini. Pada Tugas Akhir ini telah dilakukan penelitian untuk mengklasifikasikan tingkat keparahan NPDR secara otomatis dengan mengekstraksi karakteristik hard exudate menggunakan Gray Level Co–occurrence Matrix (GLCM) dan Neighborhood Gray–tone Difference Matrix (NGTDM) kemudian menentukan tingkat keparahannya menggunakan Extreme Learning Machine. Hasil akurasi tertingi yang didapat sebesar 91,22%  untuk ekstraksi ciri dengan menggunakan GLCM.

Keywords


Diabetic Retinopathy; Extreme Learning Machine; hard exudate; NPDR; Pengolahan Citra

References


http://www.who.int/campaigns/world-health-day/2016/en/ diakses pada Senin, 9 Februari 2017 pukul 11.00W.-K.

http://www.who.int/diabetes/global-report/en/ diakses pada Senin, 9 Februari 2017 pukul 11.

Vandarkuzhali, T., dan Ravichandran, DR.C.S. (2005). “ELM Based Detection of Abnormality in Retinal Image of Eye Due to Diabetic Retinopathy”. Journal of Theoretical an Applied Information Technology Vol. 66, No. 2, Hal. 423–428.

Tjandrasa, H., Putra, R. E., Wijaya, A. Y., dan Arieshanti, I. “Classification of Non-Proliferative Diabetic Retinopathy Based on Hard Exudate Usig Soft Margin SVM”. International Conference on Control System, Computing and Engineering, IEEE 2013. Hal. 376-380.

Gowthaman R. (2014). “Automatic Identification and Classification of Microaneurysms for Detection of Diabetic Retinopathy”. International Journal of Research in Engineering and Technology Vol. 3, Hal. 464-473.

Haralick, R. M., Shanmugam, K., dan Dinstein, I. (1973). “Textural Feature for Image Classification”. IEEE Transactions on System, Man and Cybernetics Vol. SMC-3, Hal. 610-621.

Amadasun, M., dan King, R. (1989). “Textural Features Corresponding to Textural Properties”. IEEE Transactions on System, Man and Cybernetics Vol. 19, Hal. 1264-1274.

Huang G. B., Zhu Q. Y., dan Siew, C. K. (2006). “Extreme Learning Machine: A new Theory and Applications”. Neurocomputing Vol. 70, Hal. 489-501.

MESSIDOR: Methods for Evaluating Segmentation and Indexing techniques Dedicated to Retinal Ophthalmology. (2004). [online] http://www.adcis.net/en/Download-Third-Party/Messidor.html diakses pada tanggal 9


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