Title: Convolutional neural network for classification of SiO₂ scanning electron microscope images

Authors: Kavitha Jayaram; G. Prakash; V. Jayaram

Addresses: Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru, India ' Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru, India ' Solid State and Structural Chemistry Unit, Indian Institute of Science, Bengaluru, India

Abstract: The recent development in deep learning has made image and speech classification and recognition tasks possible with better accuracy. An attempt was made to automatically extract required sections from literature published in journals to analyse and classify them according to their application. This paper presents high-temperature materials classification into four categories according to their wide applications such as electronic, high temperature, semiconductors, and ceramics. The challenging act is to extract SEM images' unique features as they are microscopic with different resolutions. A total of 10,000 scanning electron microscope (SEM) images are classified into two labelled categories namely crystalline and amorphous structure. The image classification and recognition process of SiO₂ was implemented using convolutional neural network (CNN) deep learning framework. Our algorithm successfully classified with a precision of 96% and accuracy of 95.5% of the test dataset of SEM images.

Keywords: deep learning; machine learning; image classification; convolution neural network; CNN; material.

DOI: 10.1504/IJBIDM.2022.123816

International Journal of Business Intelligence and Data Mining, 2022 Vol.21 No.1, pp.118 - 128

Received: 30 Jan 2021
Accepted: 02 Mar 2021
Published online: 04 Jul 2022 *

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