Automated classification of natural and synthetic pigments from SEM images using CNN-based deep learning

The development of convolutional neural network (CNN) techniques has advanced image classification in various fields, including autonomous vehicles, medical care, and cultural heritage science 1 , 2 , 3 . This study employed CNN techniques to process large numbers of micrographs, build a dataset of pigment microstructures, and classify their manufacturing processed. As research that automatically classified such micro-morphological features using CNNs remains limited, this study provides an early framework demonstrating the potential of deep learning-based morphological analysis in pigment research within cultural heritage. A dataset of 2654 SEM micrographs was established and analyzed using four architectures—AlexNet, GoogLeNet, ResNet, and VGG. Evaluation metrics such as accuracy, precision, recall, F1-score, and confusion matrix were applied, with VGG16 achieving the highest overall performance. The CNN models reached over 97% accuracy using an 80/20 split for cross-validation. These results indicate that the manufacturing process of a pigment can be inferred rapidly by comparing and classifying its micrographs.