Enhanced Material Classification via MobileSEMNet: Leveraging MobileNetV2 for SEM Image Analysis

Abstract

Background: Scanning electron microscopy (SEM) has been instrumental in elucidating material details, enabling the use of SEM images for machine learning applications. This study introduces a novel automated classification model employing deep learning techniques to classify materials using SEM imagery with high accuracy. Materials and Methods: A publicly accessible dataset comprising over 20,000 SEM images across 10 classes was utilized. The dataset was bifurcated into training and testing subsets for model development. The advanced MobileSEMNet model was trained using a pre-trained MobileNetV2 architecture, wherein deep features were extracted via the global average pooling layer. Feature extraction proceeded with fixed-size patch division using the MobileNetV2 network. During feature selection, the neighborhood component analysis (NCA) was employed to distill the most informative 512 features from the feature vector. Subsequently, a classification toolkit determined the Support Vector Machine (SVM) as the optimal shallow classifier. The model was refined using 10-fold cross-validation. Results: The primary aim to surpass the classification accuracy of the standalone MobileNetV2 was achieved; the MobileNetV2 secured a 94.85% accuracy rate, whereas the proposed MobileSEMNet model attained a 96.87% accuracy rate on the test data. Conclusions: MobileSEMNet has proven to be highly effective in the classification of SEM images within a significant dataset, signaling its potential utility in material science. The results unequivocally underscore the model's capacity for automated material type detection, thereby enhancing analytical precision and automation in the field. This work underscores the advantages of deep learning methodologies in advancing material classification in various scientific domains.