Retina Disease Classification Based on Colour Fundus Images using Convolutional Neural Networks

被引：0

作者：

Triwijoyo, Bambang Krismono ^{[1
]}

Heryadi, Yaya

Lukas

Ahmad, Adang S.

Sabarguna, Boy Subirosa

Budiharto, Widodo

Abdurachman, Edi

机构：

[1] Bina Nusantara Univ, Binus Grad Program, Comp Sci, Jl Kebon Jeruk 27, Jakarta, Indonesia

来源：

2017 INTERNATIONAL CONFERENCE ON INNOVATIVE AND CREATIVE INFORMATION TECHNOLOGY (ICITECH) | 2017年

关键词：

classification; retina disease; deep learning; CNN; BLOOD-VESSEL SEGMENTATION; MICROVASCULAR ABNORMALITIES; ATHEROSCLEROSIS RISK; AUTOMATED DETECTION; QUANTIFICATION; RETINOPATHY; STROKE; TOOL;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This paper explores Convolutional Neural Networks ( CNN) as a classifier to recognize retinal images. The dataset used in this research is public STARE color image dataset comprises of 61 x 70, 46 x 53, and 31 x 35 pixels. The dataset is categorized into 15 classes. The experimentation shows that the CNN model can achieve 80.93 percent.

引用

页数：4

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