基于卷积神经网络的视网膜分支静脉阻塞自动识别

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本文探讨了卷积神经网络（Convolutional Neural Networks, CNN）在视网膜分支静脉阻塞（Branch Retinal Vein Occlusion, BRVO）识别中的应用。BRVO是一种常见的眼科疾病，如果不及时诊断和治疗，可能严重影响视力。因此，自动化地准确识别BRVO对于医疗机构和患者都具有重要意义。研究者Runqi Zhao、Zenghai Chen 和 Zheru Chi，来自香港理工大学电子与信息工程系，提出了一种结合CNN的BRVO识别方法。他们采用了两种主要策略：一是基于图像块的处理方法，这种方法利用CNN的强大特征学习能力，从局部区域提取关键特征；二是图像级投票机制，通过整合多尺度或不同视角的特征来提高整体识别的准确性。 CNN在BRVO识别中的关键优势在于其能够自动学习和提取出抽象且有用的特征，这使得它在处理医学图像时展现出很高的识别精度。实验结果显示，他们提出的基于CNN的方法在BRVO识别上达到了超过97%的高准确率，充分证明了这种方法的有效性和实用性。文章的重点集中在以下几个方面： 1. **卷积神经网络**：作为深度学习的核心组成部分，CNN在图像识别任务中表现出色，特别是处理视觉类数据时，它能捕捉到局部和全局模式，有助于区分健康与病态的视网膜血管特征。 2. **BRVO识别挑战**：识别BRVO涉及到细微的血管变化，传统方法可能难以捕捉这些微小差异，而CNN的深度学习特性有助于解决这个问题。 3. **方法创新**：通过结合局部图像块分析和全局投票策略，研究者优化了CNN在BRVO识别中的性能，提高了识别的稳定性和鲁棒性。 4. **实验验证**：实际测试表明，他们的CNN模型在BRVO识别方面的表现优异，为医疗领域提供了一种高效且准确的辅助诊断工具。这篇研究论文介绍了如何利用CNN技术在BRVO识别中取得突破，不仅提升了诊断效率，也对提高眼科疾病的早期发现和治疗提供了强有力的支持。未来的研究可能进一步探索更深层次的网络结构优化以及与其他深度学习技术的融合，以期在医疗图像分析领域取得更大的进展。

Convolutional Neural Networks for Branch Retinal Vein

Occlusion Recognition



Runqi Zhao, Zenghai Chen, and Zheru Chi

Department of Electronic and Information Engineering

The Hong Kong Polytechnic University

Kowloon, Hong Kong

{ricky.zhao, zenghai.chen}@connect.polyu.hk; chi.zheru@polyu.edu.hk

Abstract - Branch Retinal Vein Occlusion (BRVO) is one of

the most common retinal diseases that could impair people’s

vision seriously if it is not timely diagnosed and treated. It would

save a lot of time and money for both medical institutions and

patients if BRVO could be well recognized automatically. In this

paper, we propose to exploit Convolutional Neural Networks

(CNN) for BRVO recognition. We propose patch-based method

and image-based voting method to implement the recognition. As

it could learn abstract and useful features, CNN can achieve a

high recognition accuracy. The accuracy of CNN is over 97%.

Experimental results demonstrate the efficiency of our proposed

CNN based methods for BRVO recognition.

Index Terms – Convolutional Neural Networks, Branch

Retinal Vein Occlusion, Feature Extraction.

I. INTRODUCTION

Branch retinal vein



occlusion (BRVO) is one of the most

common retinal diseases. It has a prevalence range from 0.6%

to 1.1% in the population [1]. Elder people with hypertension

or cardiovascular disease have high potential to suffer from

BRVO. BRVO would cause a great damage of human’s vision

if it is not timely diagnosed and well treated. It could lead to

blurred vision, retinal edema, or even blindness. The diagnosis

of BRVO is thus crucial. If BRVO could be recognized

automatically, ophthalmologists could save a lot effort. They

could pay more attention on the treatment. What’s more, it

will make the diagnosis inexpensive, as the hospitals could

save more money by hiring less people. Both patients and

medical institutions could benefit from it. In addition to that,

the detection of an automatic system is fast and objective.

The diagnosis of BRVO can be made by analyzing a

patient's fundus images or fluorescein angiography images. In

contrast to fluorescein angiography images, the acquisition of

fundus images is non-invasive and inexpensive. The

recognition of BRVO is thus conducted using fundus images

in this paper. Fig. 1. shows some examples of normal fundus

images and BRVO fundus images. The main components of a

normal fundus image include optic disc, vessels, and macula.

Excluding these components, the BRVO fundus images have

abnormal dark red regions, which have been pointed out by

red circles in Fig. 1. These abnormal regions are caused by



This work is supported by a Natural Science Foundation of China (NSFC)

grant (Project Code: 61473243).

retinal hemorrhages and retinal edema etc. BRVO can be

automatically recognized based on the abnormal regions.

Fig. 1. Normal and BRVO fundus images.

Convolutional Neural Networks (CNN) is a powerful tool

for image recognition. It has achieved many successes in

various fields, e.g., large-scale image classification [2], 3D

object recognition [3], scene labeling [4], and face recognition

[5] etc. One main advantage of CNN is that it can

automatically learn abstract and effective features from raw

image pixels. We do not need to manually design feature

extraction algorithms for each specific recognition task. CNN

is therefore adopted to recognize BRVO. To the best of our

knowledge, we are the only group working on BRVO

recognition. In [6], we proposed a method named Hierarchical

Local Binary Pattern (HLBP) to extract features for BRVO

classification. The superior performance of HLBP was

demonstrated by comparing to other widely-used feature

extraction methods. In this paper, we will utilize CNN to

automatically learn features for BRVO recognition, and

compare the result with HLBP and other hand-designed

feature extraction method.

The rest of this paper is organized as follows. Section II

describes our proposed CNN based methods for BRVO

recognition. Section III reports the experimental results.

Section IV concludes this paper with final remarks.

II.

METHODOLOGY

A. Convolutional Neural Networks

Fig. 2. shows a classical convolutional neural network

model. The model consists of hierarchical convolution layers

and pooling layers. For image recognition, CNN receives an

image as input. Multilayered convolution and pooling

operators are then performed on the input image. At the final

stage, the result of the last convolution layer is connected

Proceeding of the 2015 IEEE

International Conference on Information and Automation

Lijiang, China, August 2015

1633

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