SEM-Net: Deep features selections with Binary Particle Swarm Optimization Method for classification of scanning electron microscope images

Abstract

Materials Science is increasingly handling artificial intelligence methods to address the complexity in the field of everyday life necessities. Researchers in both academia and industry are interested in imaging techniques used in the characterization of nanomaterial with designed properties to meet the needs of applications in the literature. However, the increase in image size and complexity in its content restricts the use of traditional methods. Recent advances in machine learning have been used to benefit computers' potential to make sense of these images. The approach proposed in this paper aims for the feature reduction with the Binary Particle Swarm Optimization method to execute the classification process on SEM images by concatenating the deeper layers of pre-trained CNN models AlexNet and ResNet-50. The feature vectors were used as input to support vector machine classifier (SVMC) after dimension reduction to obtain the final model. Finally, the trained model's performance was tested using SEM images of Ag-doped SnO2 nanoparticles, which were prepared by the author using the low-temperature hydrothermal method. To the best of the author knowledge, these images were not available in the databases. The best accuracy value was observed with 3112 features for the SEM dataset with optimized vectors as 99.3 %. An example was illustrated where the feature selection with the BPSO technique could provide novel insight into nanoscience research and test the model with the SEM images of Ag-doped SnO2 particles that are obtained by the hydrothermal method.