多分支CNN在视网膜图像中检测和分期糖尿病视网膜病变

IF 12.3 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Worapan Kusakunniran, Sarattha Karnjanapreechakorn, Pitipol Choopong, T. Siriapisith, N. Tesavibul, N. Phasukkijwatana, S. Prakhunhungsit, Sutasinee Boonsopon
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引用次数: 1

摘要

目的本文旨在提出一种基于卷积神经网络(CNN)的方法来检测和分级视网膜图像中的糖尿病视网膜病变(DR)的解决方案。它可以将输入的视网膜图像分类为正常类别或异常类别,然后自动将其进一步划分为四个异常阶段。设计/方法论/方法所提出的解决方案是基于新提出的CNN架构,即DeepRoot开发的。它由一个主分支组成,主分支由两个分支连接。主要分支负责视网膜图像的高级和低级特征的主要特征提取器。然后,侧分支进一步从主分支输出的特征中提取更复杂和详细的特征。它们被设计为使用修改的放大/缩小和注意力层来捕捉视网膜图像中DR的小痕迹的细节。发现所提出的方法在Kaggle数据集上进行了训练、验证和测试。使用从医院的真实场景中自行收集的看不见的数据样本来评估训练模型的正则化。它在两类场景下实现了98.18%的灵敏度,具有良好的性能。独创性/价值新的基于CNN的架构(即DeepRoot)引入了多分支网络的概念。它可以帮助解决数据集不平衡的问题,特别是当不同类别(即DR的四个阶段)之间存在共同特征时。不同的类可以在网络的不同深度处输出。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detecting and staging diabetic retinopathy in retinal images using multi-branch CNN
PurposeThis paper aims to propose a solution for detecting and grading diabetic retinopathy (DR) in retinal images using a convolutional neural network (CNN)-based approach. It could classify input retinal images into a normal class or an abnormal class, which would be further split into four stages of abnormalities automatically.Design/methodology/approachThe proposed solution is developed based on a newly proposed CNN architecture, namely, DeepRoot. It consists of one main branch, which is connected by two side branches. The main branch is responsible for the primary feature extractor of both high-level and low-level features of retinal images. Then, the side branches further extract more complex and detailed features from the features outputted from the main branch. They are designed to capture details of small traces of DR in retinal images, using modified zoom-in/zoom-out and attention layers.FindingsThe proposed method is trained, validated and tested on the Kaggle dataset. The regularization of the trained model is evaluated using unseen data samples, which were self-collected from a real scenario from a hospital. It achieves a promising performance with a sensitivity of 98.18% under the two classes scenario.Originality/valueThe new CNN-based architecture (i.e. DeepRoot) is introduced with the concept of a multi-branch network. It could assist in solving a problem of an unbalanced dataset, especially when there are common characteristics across different classes (i.e. four stages of DR). Different classes could be outputted at different depths of the network.
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来源期刊
Applied Computing and Informatics
Applied Computing and Informatics Computer Science-Information Systems
CiteScore
12.20
自引率
0.00%
发文量
0
审稿时长
39 weeks
期刊介绍: Applied Computing and Informatics aims to be timely in disseminating leading-edge knowledge to researchers, practitioners and academics whose interest is in the latest developments in applied computing and information systems concepts, strategies, practices, tools and technologies. In particular, the journal encourages research studies that have significant contributions to make to the continuous development and improvement of IT practices in the Kingdom of Saudi Arabia and other countries. By doing so, the journal attempts to bridge the gap between the academic and industrial community, and therefore, welcomes theoretically grounded, methodologically sound research studies that address various IT-related problems and innovations of an applied nature. The journal will serve as a forum for practitioners, researchers, managers and IT policy makers to share their knowledge and experience in the design, development, implementation, management and evaluation of various IT applications. Contributions may deal with, but are not limited to: • Internet and E-Commerce Architecture, Infrastructure, Models, Deployment Strategies and Methodologies. • E-Business and E-Government Adoption. • Mobile Commerce and their Applications. • Applied Telecommunication Networks. • Software Engineering Approaches, Methodologies, Techniques, and Tools. • Applied Data Mining and Warehousing. • Information Strategic Planning and Recourse Management. • Applied Wireless Computing. • Enterprise Resource Planning Systems. • IT Education. • Societal, Cultural, and Ethical Issues of IT. • Policy, Legal and Global Issues of IT. • Enterprise Database Technology.
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