Metal and Metal Oxide Nanoparticle Image Analysis Using Machine Learning Algorithm

Nanomaterials are used in almost every field of engineering. Synthesis techniques and conditions greatly affect the properties of synthesized nanomaterials. Identifying the nanomaterial from FESEM and TEM images with bare eyes is an exceedingly impossible task. Digital image processing techniques play a vigorous part in identifying the size and structure, and classifying them precisely helps scientists and investigators to use them in numerous applications. The advantages of digital image processing techniques increase the precision of object recognition in computer vision and pattern recognition. The proposed technique extracts various textural features such as kurtosis, skewness, and entropy from boron, iron, and silver nanoparticle images. The classification is done by using PNN and K-NN classifiers. The K-NN classifier has an accuracy of 80.00% for boron, 86.67% for iron, and 93.33% for the silver nanoparticle images, and the PNN classifier has an accuracy of 86.67% for boron, 93.33% for iron, and 93.33% for silver nanoparticle images. Hence, based on the experimentation, the proposed study suggested that the PNN classification with texture features is the best classifier used to classify the boron, iron, and silver nanoparticle images as compared to the K-NN classifier. Further, the results also are established manually with chemical experts, which proves the exhaustiveness of the proposed method.