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EnglishIn 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%±0.0048 ) demonstrate the potential effectiveness of the proposed SoCNNet in the industrial chain of nanofibers production.
SoCNNet: An Optimized Sobel Filter Based Convolutional Neural Network for SEM Images Classification of Nanomaterials / Ieracitano, Cosimo; Paviglianiti, Annunziata; Mammone, Nadia; Versaci, Mario; Pasero, Eros Gian Alessandro; Carlo Morabito, Francesco. - ELETTRONICO. - 184:(2020), pp. 103-113. [10.1007/978-981-15-5093-5_10]
| Titolo: | SoCNNet: An Optimized Sobel Filter Based Convolutional Neural Network for SEM Images Classification of Nanomaterials | |
| Autori: | ||
| Data di pubblicazione: | 2020 | |
| Titolo del libro: | Progresses in Artificial Intelligence and Neural Systems | |
| Abstract: | In this paper an optimized deep Convolutional Neural Network (CNN) for the automatic classificati...on 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%±0.0048 ) demonstrate the potential effectiveness of the proposed SoCNNet in the industrial chain of nanofibers production. | |
| ISBN: | 978-981-15-5093-5 | |
| Appare nelle tipologie: | 2.1 Contributo in volume (Capitolo o Saggio) |
File in questo prodotto:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| electrospininig_WIRN_2019 (1).pdf | 2. Post-print / Author's Accepted Manuscript | PUBBLICO - Tutti i diritti riservati | Embargo: 10/07/2022 Richiedi una copia | |
| pdfeditoriale(1).pdf | 2a Post-print versione editoriale / Version of Record | Non Pubblico - Accesso privato/ristretto | Administrator Richiedi una copia |
http://hdl.handle.net/11583/2849884
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