Convolutional Neural Network for Cataract Maturity Classification Based LeNet

Abstract

The eyes are one of the vital organs owned by humans. One of the common eye diseases is cataracts. This disease is characterized by clouding of the lens of the eye and can interfere with vision. Worst case, sufferers can experience blindness. Cataract maturity can be divided into four categories, namely incipient, immature, mature, and hypermature. Cataracts can be removed through surgery when the cataract is in the mature or hypermature phase. Cataract examination is usually done using a slit lamp. The lack of hospitals that have this equipment can cause delays in the healing process for cataract sufferers. This study created an image processing algorithm for the maturity classification process of cataracts using the Convolutional Neural Network method with LeNet network architecture. The algorithm that has been built is capable of classifying the maturity of cataracts with an accuracy rate of 93.33%

Pengembangan Algoritma Convolutional Neural Network pada Sistem Klasifikasi Penggunaan Masker

“…Struktur jaringan LeNet merupakan salah satu model dari CNN yang awalnya digunakan sebagai sistem klasifikasi tulisan tangan berbasis citra [8]. Dalam pengembangannya, struktur jaringan LeNet juga dapat digunakan sebagai sistem klasifikasi berbagai objek seperti penyakit, jenis bau, dan juga tingkat maturitas katarak [9]- [12]. Struktur jaringan ini terdiri dari lapisan konvolusi, lapisan pooling, dan lapisan fully-connected [13]- [16].…”