SEGMENTASI AREA OPTIC DISC BERDASAR ALGORITMA DEEP LEARNING

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Latifah Listyalina, Ikhwan Mustiadi

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Abstract


Dokter akan memeriksa kondisi mata pasien dalam rangka mengetahui kualitas penglihatannya. Salah satu bagian mata yang diperika ialah kondisi pusat syaraf mata atau optic disc (OD). Hal tersebut dilakukan melalui serangkaian pengamatan pada citra retina pasien dari hasil citra kamera fundus. Namun begitu, pemeriksaan menjadi tidak efektif apabila dokter mata harus melakukan pengamatan secara manual pada citra retina dengan jumlah yang banyak. Dalam hal ini, kesalahan pengamatan sangat mungkin terjadi. Lebih lanjut, tidak menutup kemungkinan bahwa hasil pengamatan oleh seorang dokter dengan dokter lainnya menunjukkan hasil yang berbeda, atau dengan kata lain hasil pengamatan manual dapat bersifat subyektif. Dengan latar belakang masalah yang telah dijelaskan, peneitian ini bertujuan untuk merancang sebuah program komputer guna membantu dokter melakukan segmentasi optic disc (OD) secara simultan dan akurat. Algoritma segmentasi OD dilatih dan diuji pada 800 buah citra retina dari basis data REFUGE selama 150 epoh menggunakan algoritma optimasi adaptive moment estimation (Adam) optimizer. Proses pelatihan dan evaluasi dilakukan pada CPU dengan spesifikasi prosesor Inter Xeon @2,30 GHz dan sebuah GPU NVIDIA Tesla T4 yang disediakan secara gratis oleh Google’s Colab. Hasil penelitian menunjukkan bahwa algoritma yang diusulkan telah mampu memisahkan area OD dengan area lain pada citra retina dengan baik.

Keywords


deep learning; optic disc; segmentasi

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