Deep Learning untuk Klasifikasi Glaukoma dengan menggunakan Arsitektur EfficientNet

WAHYUNI RIZKY PERDANI, RITA MAGDALENA, NOR KUMALASARI CAECAR PRATIWI

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ABSTRAK

Glaukoma merupakan kerusakan yang terjadi pada saraf mata yang disebabkan oleh meningkatnya tekanan di bola mata. Glaukoma dapat menyebabkan penderitanya mengalami kebutaan permanen. Data dari WHO, jumlah orang yang diperkirakan menjadi buta akibat glaukoma primer adalah 4,5 juta. Penilaian klasifikasi tingkatan glaukoma oleh ophthalmologist menggunakan nilai CDR (Cup to Disc Ratio). Maka dari itu, dibuat sistem yang dapat digunakan dalam mengklasifikasikan glaukoma melalui citra fundus mata dengan menggunakan metode CNN (Convolutional Neural Network) dengan arsitektur EfficientNet. Klasifikasi glaukoma dibagi menjadi 5 kelas, yaitu deep, early, moderate, OHT dan normal. Citra mata yang digunakan didapatkan dari dataset RimOne r1. Penelitian ini mencari sistem dengan performansi terbaik. Model yang mendapatkan parameter performansi terbaik adalah citra dengan hyperparameter optimizer Adamax, learning rate 0,001, epoch 20, dan batch size 32. Akurasi, presisi, recall, dan F1-Score masing-masing mencapai 1,0000.

Kata kunci: Glaukoma, Convolutional Neural Network (CNN), EfficientNet

 

ABSTRACT

Glaucoma is the optic nerve damage caused by increasing pressure on the eyeball. Glaucoma can cause patients to encounter permanent blindness. According to WHO data, the number of people estimated to be blind from primary glaucoma is 4,5 million. Evaluation of glaucoma grade classification by ophthalmologist uses CDR (Cup to Disc Ratio) value. Therefore, a system has been created that can be used to classify glaucoma through eye fundus images using the CNN (Convolutional Neural Network) method with EfficientNet architecture. Glaucoma is classified into 5 classes, namely deep, early, moderate, OHT and normal. The used eye image is obtained from the RimOne r1 dataset. This research is looking for a system with the best performance. The model that got the best performance parameters with the hyperparameter optimizer Adamax, learning rate 0,001, epoch 20, and batch size 32. Accuracy, precision, recall, and F1-Score each reached 1,0000.

Keywords: Glaucoma, Convolutional Neural Network (CNN), EfficientNet


Kata Kunci


Glaukoma; Deep Learning; Convolutional Neural Network (CNN); EfficientNet

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Referensi


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DOI: https://doi.org/10.26760/elkomika.v10i2.322

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