图像预处理在糖尿病眼病分类与识别中的应用。

IF 5.1 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Data Science and Engineering Pub Date : 2021-01-01 Epub Date: 2021-08-17 DOI:10.1007/s41019-021-00167-z
Rubina Sarki, Khandakar Ahmed, Hua Wang, Yanchun Zhang, Jiangang Ma, Kate Wang
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引用次数: 38

摘要

糖尿病性眼病(DED)是困扰糖尿病患者的一系列眼病。在视网膜眼底图像中识别DED是一项至关重要的活动,因为早期诊断和治疗最终可以将视力损害的风险降至最低。视网膜眼底图像对早期DED的分类和鉴别具有重要意义。利用视网膜眼底图像建立准确的诊断模型在很大程度上取决于图像的质量和数量。本文系统地研究了图像处理对DED分类的意义。本文提出的DED自动分类框架分为以下几个步骤:图像质量增强、图像分割(感兴趣区域)、图像增强(几何变换)和分类。采用传统的图像处理方法,采用一种新的卷积神经网络(CNN)架构,获得了最优的图像处理效果。新构建的CNN与传统的图像处理方法相结合,在DED分类问题上表现出最好的性能和准确率。所进行的实验结果显示出足够的准确性、特异性和敏感性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Image Preprocessing in Classification and Identification of Diabetic Eye Diseases.

Diabetic eye disease (DED) is a cluster of eye problem that affects diabetic patients. Identifying DED is a crucial activity in retinal fundus images because early diagnosis and treatment can eventually minimize the risk of visual impairment. The retinal fundus image plays a significant role in early DED classification and identification. An accurate diagnostic model's development using a retinal fundus image depends highly on image quality and quantity. This paper presents a methodical study on the significance of image processing for DED classification. The proposed automated classification framework for DED was achieved in several steps: image quality enhancement, image segmentation (region of interest), image augmentation (geometric transformation), and classification. The optimal results were obtained using traditional image processing methods with a new build convolution neural network (CNN) architecture. The new built CNN combined with the traditional image processing approach presented the best performance with accuracy for DED classification problems. The results of the experiments conducted showed adequate accuracy, specificity, and sensitivity.

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来源期刊
Data Science and Engineering
Data Science and Engineering Engineering-Computational Mechanics
CiteScore
10.40
自引率
2.40%
发文量
26
审稿时长
12 weeks
期刊介绍: The journal of Data Science and Engineering (DSE) responds to the remarkable change in the focus of information technology development from CPU-intensive computation to data-intensive computation, where the effective application of data, especially big data, becomes vital. The emerging discipline data science and engineering, an interdisciplinary field integrating theories and methods from computer science, statistics, information science, and other fields, focuses on the foundations and engineering of efficient and effective techniques and systems for data collection and management, for data integration and correlation, for information and knowledge extraction from massive data sets, and for data use in different application domains. Focusing on the theoretical background and advanced engineering approaches, DSE aims to offer a prime forum for researchers, professionals, and industrial practitioners to share their knowledge in this rapidly growing area. It provides in-depth coverage of the latest advances in the closely related fields of data science and data engineering. More specifically, DSE covers four areas: (i) the data itself, i.e., the nature and quality of the data, especially big data; (ii) the principles of information extraction from data, especially big data; (iii) the theory behind data-intensive computing; and (iv) the techniques and systems used to analyze and manage big data. DSE welcomes papers that explore the above subjects. Specific topics include, but are not limited to: (a) the nature and quality of data, (b) the computational complexity of data-intensive computing,(c) new methods for the design and analysis of the algorithms for solving problems with big data input,(d) collection and integration of data collected from internet and sensing devises or sensor networks, (e) representation, modeling, and visualization of  big data,(f)  storage, transmission, and management of big data,(g) methods and algorithms of  data intensive computing, such asmining big data,online analysis processing of big data,big data-based machine learning, big data based decision-making, statistical computation of big data, graph-theoretic computation of big data, linear algebraic computation of big data, and  big data-based optimization. (h) hardware systems and software systems for data-intensive computing, (i) data security, privacy, and trust, and(j) novel applications of big data.
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