Classification of Metal and Metal Oxide Nanoparticles Using Machine Learning and Deep Learning Models

Abstract

Abstract For decades classification of objects based on texture has portrayed a prominent role in the applications of computer vision and image processing. The need for automated classification of objects sharing similar visual appearance is growing day by day in various industries like metal, paper, wood, etc. The initial task of any metal devising industry is to classify the metals before they are used in building any devices. For ages, traditional approaches are used to identify the metals using their properties which is time-consuming and not economical. In the proposed study an automated model for texture analysis and classification of the metal and metal oxide nanoparticles is developed using machine learning and deep learning concepts to overcome the drawbacks of the traditional approach. The machine learning model uses KNN and PNN classifiers, and the deep learning model uses LeNet, and ConvXGB classifiers to analyze the texture and classify them as metals (silver, boron) and metal oxides (iron oxide, copper oxide). From the experiment it is found that the average accuracy using the KNN classifier is 70.00%, the PNN classifier is 75.00%, LeNet is 95.00%, and ConvXGB yields 85.00%. It is analyzed that the LeNet has the highest accuracy of 95%, and hence, it is suitable for the classification of silver, boron, iron oxide, and copper oxide nanoparticle images.