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

Cosimo Ieracitano¹, Nadia Mammone¹, Annunziata Paviglianiti²

¹Department of Civil Engineering, Energy Environment and Materials, University Mediterranea of Reggio Calabria, Via Graziella Feo di Vito, Reggio, Calabria 89124, Italy.

International Journal of Neural Systems

|October 14, 2022

Summary

This summary is machine-generated.

This study introduces a generative model and transfer learning system to classify Scanning Electron Microscope images of nanofibers. The approach achieves high accuracy, potentially reducing costly electrospinning experiments.

Keywords:

Deep learning classification convolutional neural networks electrospinning generative adversarial networks nanomaterials transfer learning

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Area of Science:

Materials Science and Engineering
Artificial Intelligence
Image Analysis

Background:

Accurate classification of nanofibers is crucial for quality control in electrospinning.
Generating sufficient training data for image classification can be challenging and resource-intensive.

Purpose of the Study:

To develop a generative model for creating synthetic Scanning Electron Microscope (SEM) images of defective and nondefective nanofibers.
To implement a transfer learning strategy for classifying these SEM images.
To assess the effectiveness of the proposed system in reducing the need for extensive laboratory experiments.

Main Methods:

Development of a conditional-Generative Adversarial Network (c-GAN) to generate synthetic SEM images of nanofibers.
Implementation of a transfer learning approach using a pre-trained Convolutional Neural Network (CNN).
Training the CNN on synthetic images and validating its performance on real SEM images.

Main Results:

The transfer-learned CNN achieved an accuracy rate of up to 95.31% in classifying SEM images.
The generative model successfully produced synthetic images suitable for training classification models.
The system demonstrated potential for reducing the number of required laboratory electrospinning experiments.

Conclusions:

The proposed generative model and transfer learning system offer an effective solution for classifying SEM images of nanofibers.
This approach can significantly decrease the cost and time associated with industrial-scale electrospinning experiments.
The findings support the adoption of AI-driven methods for quality control in nanofiber production.