Automated Detection of Central Retinal Vein Occlusion Using Convolutional Neural Network

Abstract

The Central Retinal Vein Occlusion (CRVO) is the next supreme reason for the vision loss among the elderly people, after the Diabetic Retinopathy. The CRVO causes abrupt, painless vision loss in the eye that can lead to visual impairment over the time. Therefore, the early diagnosis of CRVO is very important to prevent the complications related to CRVO. But, the early symptoms of CRVO are so subtle that manually observing those signs in the retina image by the ophthalmologists is difficult and time consuming process. There are automatic detection systems for diagnosing ocular disease, but their performance depends on various factors. The haemorrhages, the early sign of CRVO, can be of different size, color and texture from dot haemorrhages to flame shaped. For reliable detection of the haemorrhages of all types; multifaceted pattern recognition techniques are required. To analyse the tortuosity and dilation of the veins, complex mathematical analysis is required in order to extract those features. Moreover, the performance of such feature extraction methods and automatic detection system depends on the quality of the acquired image. In this chapter, we have proposed a prototype for automated detection of the CRVO using the deep learning approach. We have designed a Convolutional Neural Network (CNN) to recognize the retina with CRVO. The advantage of using CNN is that no extra feature extraction step is required. We have trained the CNN to learn the features from the retina images having CRVO and classify them from the normal retina image. We have obtained an accuracy of 97.56% for the recognition of CRVO.