Nanoparticles Ordering Classification Using Deep Convolutional Neural Networks

Intelligent neural networks are used efficiently for image classification and recognition in various scientific areas. One of the most important of these areas is nanoscience. Researchers are currently seeking to apply various deep learning neural networks models for fast and intelligent prediction and recognition of nanostructures based on scanning electron microscopy images. Models of Deep Convolutional Neural Networks (DCNN) have reached a high accuracy rate in nanoparticles classification and recognition. In fact, the improvement of the classification accuracy strongly relies on the perfect fine-tuning of image data and model parameters and that is what this research has worked for. The aim of this paper is to present a model, specifically the VGG16 convolutional neural network model, for high accurate nanoparticles ordering classification. The model has been used to classify the nanoparticles ordering using a typical dataset of electron microscopy images. In this research, an experiment has been carried out to achieve better accuracy rate in comparison to previously recorded accuracy rates. Data augmentation, modification techniques, and model tuning parameters are applied to excess the ability of the model for classifying the input image to ordered or non-ordered nanoparticles. Compared to the related works, the presented model has outperformed the pervious by achieving an accuracy rate of 97%. In this work, it has been observed that training iterations and balanced training data significantly improve the model performance and enhance the accuracy rate.