A Conditional Generative Adversarial Network and Transfer Learning-Oriented Anomaly Classification System for Electrospun Nanofibers.

IF 6.6 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
International Journal of Neural Systems Pub Date : 2022-12-01 Epub Date: 2022-10-13 DOI:10.1142/S012906572250054X
Cosimo Ieracitano, Nadia Mammone, Annunziata Paviglianiti, Francesco Carlo Morabito
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引用次数: 4

Abstract

This paper proposes a generative model and transfer learning powered system for classification of Scanning Electron Microscope (SEM) images of defective nanofibers (D-NF) and nondefective nanofibers (ND-NF) produced by electrospinning (ES) process. Specifically, a conditional-Generative Adversarial Network (c-GAN) is developed to generate synthetic D-NF/ND-NF SEM images. A transfer learning-oriented strategy is also proposed. First, a Convolutional Neural Network (CNN) is pre-trained on real images. The transfer-learned CNN is trained on synthetic SEM images and validated on real ones, reporting accuracy rate up to 95.31%. The achieved encouraging results endorse the use of the proposed generative model in industrial applications as it could reduce the number of needed laboratory ES experiments that are costly and time consuming.

基于条件生成对抗网络和迁移学习的电纺纳米纤维异常分类系统。
本文提出了一种基于生成模型和迁移学习驱动的系统,用于静电纺丝(ES)工艺生产的缺陷纳米纤维(D-NF)和非缺陷纳米纤维(ND-NF)的扫描电镜图像分类。具体来说,开发了一种条件生成对抗网络(c-GAN)来生成合成的D-NF/ND-NF SEM图像。提出了以迁移学习为导向的策略。首先,对真实图像进行卷积神经网络(CNN)的预训练。迁移学习的CNN在合成的SEM图像上进行训练,并在真实图像上进行验证,准确率高达95.31%。取得的令人鼓舞的结果支持在工业应用中使用所提出的生成模型,因为它可以减少所需的实验室ES实验的数量,这些实验既昂贵又耗时。
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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来源期刊
International Journal of Neural Systems
International Journal of Neural Systems 工程技术-计算机:人工智能
CiteScore
11.30
自引率
28.80%
发文量
116
审稿时长
24 months
期刊介绍: The International Journal of Neural Systems is a monthly, rigorously peer-reviewed transdisciplinary journal focusing on information processing in both natural and artificial neural systems. Special interests include machine learning, computational neuroscience and neurology. The journal prioritizes innovative, high-impact articles spanning multiple fields, including neurosciences and computer science and engineering. It adopts an open-minded approach to this multidisciplinary field, serving as a platform for novel ideas and enhanced understanding of collective and cooperative phenomena in computationally capable systems.
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