系统文献综述:深度学习检测白内障

S. Masruroh, Daffa Aditya Rahman, Rizka Amalia Putri
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引用次数: 0

摘要

白内障是一种眼部疾病,如果不及时治疗,可能会导致失明。许多研究提出了一种检测白内障的系统,以减少早期失明的风险。为了找出使用什么方法来创建白内障检测系统,有必要对讨论基于深度学习的白内障检测的研究进行系统文献综述(SLR)。在提出的每种方法中,使用CNN算法从头开始建模的方法是使用的大多数方法。此外,使用的其他方法包括迁移学习,微调迁移学习,最终分类器和预训练模型也是使用的方法。每种方法都有其优点和缺点。从所获得的结果来看,使用一种方法及其组合必须注意许多因素,例如所使用的方法及其组合是否符合所要解决的目标和问题,了解可能影响模型性能的局限性必须是研究人员所关注的,才能产生准确的模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Systematic Literature Review: Detecting Cataract With Deep Learning
A cataract is an eye disease that can lead to blindness if left untreated. Many studies have proposed a system for detecting cataracts to reduce the risk of early blindness. To find out what method is used to create a cataract detection system, it is necessary to make a Systematic Literature Review (SLR) on studies that discuss deep learning-based cataract detection. Of each method proposed, the approach to modeling from scratch using the CNN algorithm is the majority of the methods used. In addition, other methods used include Transfer Learning, Fine Tuning Transfer Learning, Final Classifier, and pre-trained models are also the methods used. Each method has its advantages and disadvantages. From the results obtained, the use of a method and its combination must pay attention to many factors such as whether the method used and its combination is by the objectives and problems to be solved, understanding the limitations that can affect the performance of the model must be the concern of researchers to produce an accurate model.
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