基于VGG-19模型的卷积神经网络白内障检测

Md. Sajjad Mahmud Khan, Mahiuddin Ahmed, Raseduz Zaman Rasel, Mohammad Monirujjaman Khan
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引用次数: 26

摘要

白内障是世界范围内视力损害和失明的主要原因之一。大约有50%的人失明。因此,早期发现和预防白内障可以减少视力损害和失明。与白内障不同,人工智能(AI)在青光眼、黄斑变性、糖尿病视网膜病变、角膜状况、年龄相关眼病等眼科领域的进展相当丰硕。现有的白内障检测方法大多是基于传统的机器学习方法。另一方面,人工提取视网膜特征是一个耗时的过程,需要专业的眼科医生。为此,我们提出了一种基于卷积神经网络的彩色眼底图像白内障检测模型VGG19。
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Cataract Detection Using Convolutional Neural Network with VGG-19 Model
Cataract is one of the prevalent causes of visual impairment and blindness worldwide. There is around 50% of overall blindness. Therefore, an early detection and prevention of cataract may reduce the visual impairment and the blindness. The advancement of Artificial Intelligence (AI) in the field of ophthalmology such as glaucoma, macular degeneration, diabetic retinopathy, corneal conditions, age related eye diseases is quite fruitful unlike cataract. Most of the existing approaches on cataract detection are based on traditional machine learning methods. On the other hand, the manual extraction of retinal features is a time-consuming process and requires an expert ophthalmologist. So, we proposed a model VGG19 which is a convolutional neural network model to detect the cataract by using color fundus images.
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