Abstract
In this paper an optimized deep Convolutional Neural Network (CNN) for the automatic classification of Scanning Electron Microscope (SEM) images of homogeneous (HNF) and nonhomogeneous nanofibers (NHNF) produced by electrospinnig process is presented. Specifically, SEM images are used as input of a Deep Learning (DL) framework consisting of: a Sobel filter based pre-processing stage followed by a CNN classifier. Here, such DL architecture is denoted as SoCNNet. The Polyvinylacetate (PVAc) SEM image of NHNF and HNF dataset collected at the Materials for Environmental and Energy Sustainability Laboratory of the University Mediterranea of Reggio Calabria (Italy) is used to evaluate the performance of the developed system. Experimental results (average accuracy rate up to \(80.27\% \pm 0.0048\)) demonstrate the potential effectiveness of the proposed SoCNNet in the industrial chain of nanofibers production.
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Notes
- 1.
Fuzzy divergence can be considered as a distance because it satisfies all the axioms of the metric spaces.
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Acknowledgments
This work is supported by the project code: GR-2011-02351397. The authors would also like to thank the research group of the Materials for Environmental and Energy Sustainability Laboratory from the University Mediterranea of Reggio Calabria (Italy) for providing the SEM image dataset used in this work.
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Ieracitano, C., Paviglianiti, A., Mammone, N., Versaci, M., Pasero, E., Morabito, F.C. (2021). SoCNNet: An Optimized Sobel Filter Based Convolutional Neural Network for SEM Images Classification of Nanomaterials. In: Esposito, A., Faundez-Zanuy, M., Morabito, F., Pasero, E. (eds) Progresses in Artificial Intelligence and Neural Systems. Smart Innovation, Systems and Technologies, vol 184. Springer, Singapore. https://doi.org/10.1007/978-981-15-5093-5_10
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