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A Deep Learning-Based Classification Study for Diagnosing Corneal Ulcers on Ocular Staining Images 基于深度学习的角膜溃疡染色图像分类诊断研究
Pub Date : 2023-08-14 DOI: 10.26650/acin.1173465
Onur Sevli
Corneal ulcer is a common disease worldwide and is one of the leading causes of corneal blindness. Diagnosis of the disease requires expertise, and the number of experienced ophthalmologists is not sufficient, especially in underdeveloped countries. For this reason, it is necessary to develop technology-based decision support systems in the diagnosis of the disease. However, the number of studies on this subject is not sufficient. In this study, CNN-based classifications were performed using corneal ulcer images obtained by an ocular staining technique, consisting of 712 samples and three classes. In addition to the AlexNet and VGG16 state-of-the-art architectures, which are widely used in the literature, a CNN model proposed for this study was used for classification. In the classifications performed by applying data augmentation, 95.34% accuracy with AlexNet, 98.14% with VGG16, and 100% accuracy with the proposed model was obtained. The findings were compared with similar studies in the literature. It was concluded that the accuracy rates obtained with all of the models used in the study were generally higher than similar studies in the literature, and the accuracy obtained with the proposed CNN model was higher than all of the peers. In addition, the success of the proposed model compared to other models with more complex structures revealed that it is not always necessary to use complex architectures for high accuracy.
角膜溃疡是一种世界性的常见病,是导致角膜失明的主要原因之一。这种疾病的诊断需要专业知识,而有经验的眼科医生数量不足,特别是在不发达国家。因此,有必要开发基于技术的疾病诊断决策支持系统。然而,关于这一主题的研究数量还不够。在本研究中,基于cnn的分类使用眼部染色技术获得的角膜溃疡图像,包括712个样本和三个类别。除了文献中广泛使用的AlexNet和VGG16最先进的架构外,本研究还使用了为本研究提出的CNN模型进行分类。在应用数据增强进行分类时,AlexNet的准确率为95.34%,VGG16的准确率为98.14%,所提出的模型的准确率为100%。研究结果与文献中的类似研究进行了比较。可以得出结论,本研究中使用的所有模型的准确率普遍高于文献中类似的研究,并且本文所提出的CNN模型的准确率高于所有同类模型。此外,与其他具有更复杂结构的模型相比,所提出的模型的成功表明,并不总是需要使用复杂的体系结构来获得高精度。
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引用次数: 0
Can the Artificial Intelligence Speak? Subalternity of “Subontologies” and the Death of the Programmer 人工智能会说话吗?“子本体”的次择性与程序员的死亡
Pub Date : 2023-06-27 DOI: 10.26650/acin.1279545
Haktan Kalir
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引用次数: 0
LISS’nAP: A Peer-Advising Collaborative Learning Approach for Object-Oriented Programming Learning LISS 'nAP:面向对象编程学习的同伴建议协作学习方法
Pub Date : 2023-06-26 DOI: 10.26650/acin.869786
Rochdi Boudjehem, Ali Benyounes, Y. Lafifi
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引用次数: 0
Social Media Sentiment Analysis for Solar Eclipse with Text Mining 基于文本挖掘的日食社交媒体情感分析
Pub Date : 2023-06-20 DOI: 10.26650/acin.1207100
Adem Korkmaz, Selma Bulut
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引用次数: 0
Malware Detection in Forensic Memory Dumps: The Use of Deep Meta-Learning Models 取证内存转储中的恶意软件检测:深度元学习模型的使用
Pub Date : 2023-06-20 DOI: 10.26650/acin.1282824
Y. Özkan
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引用次数: 0
A Modular Efficiency Determination Formula for Information Retrieval Evaluations and Optimizations 信息检索评价与优化的模块化效率确定公式
Pub Date : 2023-06-19 DOI: 10.26650/acin.1198925
V. Budak
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引用次数: 0
Machine Learning Models on MOBA Gaming: League of Legends Winner Prediction MOBA游戏中的机器学习模型:《英雄联盟》赢家预测
Pub Date : 2023-06-15 DOI: 10.26650/acin.1180583
Kaan Arik
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引用次数: 0
An Intrusion Detection Approach based on the Combination of Oversampling and Undersampling Algorithms 一种过采样和欠采样相结合的入侵检测方法
Pub Date : 2023-06-14 DOI: 10.26650/acin.1222890
A. Arik, G. Cavdaroglu
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引用次数: 0
Melting of Privacy with Machine Learning, Big Data, and Social Media 机器学习、大数据和社交媒体融合隐私
Pub Date : 2023-06-14 DOI: 10.26650/acin.1231944
Pelin Canbay, Zübeyde Demircioğlu
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引用次数: 0
Analysis of City Demographics in Turkiye Using Data Mining Techniques 基于数据挖掘技术的土耳其城市人口统计分析
Pub Date : 2023-06-13 DOI: 10.26650/acin.1132510
Özge Doğuç, Kevser Sahinbas, Gökhan Silahtaroglu
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引用次数: 0
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