Severity Classification of Non-Proliferative Diabetic Retinopathy Using Convolutional Support Vector Machine

Putra, Ricky Eka and Tjandrasa, Handayani and Suciati, Nanik Severity Classification of Non-Proliferative Diabetic Retinopathy Using Convolutional Support Vector Machine. International Journal of Intelligent Engineering and Systems.

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Abstract

Diabetic retinopathy merupakan salah satu penyakit komplikasi dari diabetes mellitus yang menyerang mata manusia. Tingkat resiko terbesar dari penyakit ini adalah kebutaan. Non-Proliferative Diabetic Retinopathy merupakan salah satu jenis diabetic retinopathy pada tahap awal dengan ditandai tidak adanya pertumbuhan pembuluh darah baru. Tingkat keparahan Non-Proliferative Diabetic Retinopathy dapat dibedakan menjadi tiga yakni mild, moderate, dan severe. Tingkat keparahan dari penderita diabetic retinopathy perlu diketahui lebih dini agar dapat dilakukan penanganan secepatnya. Pengelompokan Non-Proliferative Diabetic Retinopathy melibatkan dua cara yakni secara konvensional dan inkonvesional. Pada proses pendeteksian memerlukan segmentasi lesi-lesi tersebut, antara lain microaneurysms, hemorrhage, dan exudates. Proses segmentasi tersebut menggunakan metode morfologi matematika yang merupakan metode konvensional. Selanjutnya, proses klasifikasi tingkat keparahan diabetic retinopathy menggunakan metode Adaptive Neuro Fuzzy Inference System dengan berdasarkan fitur exudates. Selain itu, klasifikasi tingkat keparahan Non-Proliferative Diabetic Retinopathy menggunakan cara inkonvensional dapat dilakukan dengan memanfaatkan deep learning. Pra-proses dalam klasifikasi ini memanfaatkan tiga metode Adaptive Histogram Equalization, Morphology Contrast Enhancement, dan Homomorphic untuk meningkatkan kontras citra. Kemudian metode Convolutional Neural Network dengan arsitektur GoogLeNet, ResNet18, ResNet50, dan ResNet101 digunakan sebagai pengekstraksian fitur serta melakukan pereduksian fitur menggunakan Relief dan Principal Component Analysis. Selanjutnya, metode Support Vector Machine Naïve Bayes digunakan sebagai classifier pada proses klasifikasi ini. Hasil akurasi terbaik klasifikasi yang diperoleh dengan menggunakan cara konvensional adalah 85.06%, sedangkan hasil akurasi terbaik pada cara inkonvensional diperoleh dengan menggunakan metode pra-proses Homomorphic dan Morphology Contrast Enhancement serta relief sebagai reduksi fiturnya. Nilai akurasi terbaik yang dapat dicapai dengan menerapkan metode-metode tersebut adalah 87.5% ============================================================================================= Diabetic retinopathy is a complication of diabetes mellitus that attacks the eyes of human. The greatest risk level from this disease is blindness. NonProliferative Diabetic Retinopathy is one type of diabetic retinopathy in the early stages with the absence of new blood vessel growth. The severity of NonProliferative Diabetic Retinopathy can be divided into three namely mild, moderate, and severe. The severity of diabetic retinopathy patients needs to be known early to get some treatments as so as possible. Non-Proliferative Diabetic Retinopathy grouping is done in two ways namely conventional and unconventional methods. In the first way, this detection process requires segmentation of lesions, including microaneurysms, hemorrhage, and exudates. The segmentation process uses mathematical morphology which is a conventional method. Furthermore, the severity classification process of diabetic retinopathy implements the Adaptive Neuro Fuzzy Inference System based on the features of exudates. In addition, the classification of the severity of Non-Proliferative Diabetic Retinopathy using unconventional methods can be done by utilizing deep learning method. Pre-processing in this classification utilizes three methods of Adaptive Histogram Equalization, Morphology Contrast Enhancement, and Homomorphic to improve the image contrast. Then, the Convolutional Neural Networks with GoogLeNet, ResNet18, ResNet50, and ResNet101 architectures are used for feature extraction and feature reduction are performed using Relief and Principal Component Analysis. The Support Vector Machine-Naïve Bayes method is used as a classifier in this classification process. The best result of this study obtained by using conventional methods is 85.06%, while the best result in unconventional methods is obtained by using Homomorphic and Morphological Contrast Enhancement methods and Relief as a feature reduction. The best accuracy that can be achieved by applying these methods is 87.5%.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science. EDP
Q Science > QA Mathematics > QA76 Computer software
Q Science > QA Mathematics > QA76.6 Computer programming.
Q Science > QA Mathematics > QA76.87 Neural networks (Computer Science)
T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5105.546 Computer algorithms
Depositing User: Ricky Eka Putra
Date Deposited: 25 Aug 2020 05:24
Last Modified: 25 Aug 2020 05:31
URI: https://repository.its.ac.id/id/eprint/80816

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