dag-cnn结构对老年性黄斑变性的分类

IF 0.6 Q4 ENGINEERING, BIOMEDICAL
S. Sabi, J. Jacob, V. Gopi
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引用次数: 0

摘要

在世界主要国家,年龄相关性黄斑变性(AMD)是视力损害的主要原因。因此,准确的早期发现该疾病对该领域的更多研究至关重要。此外,进行彻底的眼部诊断来检测AMD是一项复杂的工作。本文介绍了一种基于有向无环图(DAG)结构的卷积神经网络(CNN)架构,以更好地对干湿AMD进行分类。DAG架构可以组合来自多个层的功能,以提供更好的结果。DAG模型还具有学习多层次视觉属性以提高分类精度的能力。对基于dag的CNN模型进行微调有助于提高网络的性能。利用Mendeley数据集对该模型进行训练和测试,准确率达到99.2%,AUC值为0.9999。该模型在精度、召回率和f1分数等参数上也取得了较好的结果。该网络的性能还与在同一数据集上进行的相关工作进行了比较。这表明所提出的方法能够对AMD图像进行分级,以帮助早期发现该疾病。该模型在实时应用中也具有计算效率,因为它使用较少的可学习参数和较少的浮点操作(FLOPs)来进行分类过程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CLASSIFICATION OF AGE-RELATED MACULAR DEGENERATION USING DAG-CNN ARCHITECTURE
Age-related Macular Degeneration (AMD) is the prime reason for vision impairment observed in major countries worldwide. Hence an accurate early detection of the disease is vital for more research in this area. Also, having a thorough eye diagnosis to detect AMD is a complex job. This paper introduces a Directed Acyclic Graph (DAG) structure-based Convolutional Neural network (CNN) architecture to better classify Dry or Wet AMD. The DAG architecture can combine features from multiple layers to provide better results. The DAG model also has the capacity to learn multi-level visual properties to increase classification accuracy. Fine tuning of DAG-based CNN model helps in improving the performance of the network. The training and testing of the proposed model are carried out with the Mendeley data set and achieved an accuracy of 99.2% with an AUC value of 0.9999. The proposed model also obtains better results for other parameters such as precision, recall and F1-score. Performance of the proposed network is also compared to that of the related works performed on the same data set. This shows ability of the proposed method to grade AMD images to help early detection of the disease. The model also performs computationally efficient for real-time applications as it does the classification process with few learnable parameters and fewer Floating-Point Operations (FLOPs).
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来源期刊
Biomedical Engineering: Applications, Basis and Communications
Biomedical Engineering: Applications, Basis and Communications Biochemistry, Genetics and Molecular Biology-Biophysics
CiteScore
1.50
自引率
11.10%
发文量
36
审稿时长
4 months
期刊介绍: Biomedical Engineering: Applications, Basis and Communications is an international, interdisciplinary journal aiming at publishing up-to-date contributions on original clinical and basic research in the biomedical engineering. Research of biomedical engineering has grown tremendously in the past few decades. Meanwhile, several outstanding journals in the field have emerged, with different emphases and objectives. We hope this journal will serve as a new forum for both scientists and clinicians to share their ideas and the results of their studies. Biomedical Engineering: Applications, Basis and Communications explores all facets of biomedical engineering, with emphasis on both the clinical and scientific aspects of the study. It covers the fields of bioelectronics, biomaterials, biomechanics, bioinformatics, nano-biological sciences and clinical engineering. The journal fulfils this aim by publishing regular research / clinical articles, short communications, technical notes and review papers. Papers from both basic research and clinical investigations will be considered.
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