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Deteksi Glaukoma pada Citra Fundus Retina menggunakan Metode Local Binary Pattern dan Support Vector Machine
Abstract
ABSTRAK
Glaukoma merupakan sebuah penyakit yang menyerang indera penglihatan dan dapat mengakibatkan kebutaan yang bersifat permanen. Meskipun penyakit ini tidak bisa disembuhkan, tetapi gejala kerusakannya dapat diminimalkan dengan melakukan pendeteksian secara dini. Deteksi glaukoma dapat dilakukan secara manual oleh oftalmologis, tetapi metode ini terbilang subyektif sebab hasil pengamatannya bergantung pada domain pengetahuan dokter, sementara di sisi lain teknik pencitraan medis modern, seperti OCT, CSLO, dan HRT berbiaya tinggi dan ketersediaan perangkatnya relatif terbatas. Pada penelitian ini, sebuah sistem berbasis machine learning untuk mendeteksi glaukoma pada citra fundus retina telah dirancang melalui proses pengolahan citra digital menggunakan metode Local Binary Pattern dan Support Vector Machine. Performansi sistem diuji pada 146 citra fundus yang terdiri dari citra fundus sehat dan glaukoma. Dengan menggunakan metode yang diusulkan, sistem mampu memberikan tingkat akurasi terbaik pada 93.15%, sensitivitas 92.30%, dan spesifisitas 93.61%.
Kata kunci: Glaukoma, Local Binary Pattern, Support Vector Machine
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ABSTRACT
Glaucoma is a disease that attacks the sense of sight and can lead to permanent blindness. Although this disease cannot be cured, the symptoms of the damage can be minimized by early detection. Glaucoma detection can be done manually by an ophthalmologist, but this method is somewhat subjective because the results of the observations depend on the domain of the doctor’s knowledge, whereas onthe other hand, modern medical imaging techniques, such as OCT, CSLO, and HRT, are high in cost and the availability of devices is relatively limited. This research proposed a machine learning-based system to detect glaucoma in retinal fundus images through digital image processing using the Local Binary Pattern and Support Vector Machine methods. The performance of the system was tested on 146 fundus images consisting of healthy fundus images and glaucoma. By using the proposed method, the system provide the best accuracy rate at 93.15%, sensitivity 92.30%, and specificity 93.61%.
Keyword: Glaucoma, Local Binary Pattern, Support Vector Machine
Keywords
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DOI: https://doi.org/10.26760/elkomika.v10i4.769
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ISSN (print) : 2338-8323 | ISSN (electronic) : 2459-9638
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Department of Electrical Engineering Institut Teknologi Nasional Bandung, Indonesia
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Contact: +627272215 (ext. 206)
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