Classification of scanning electron microscope images of pharmaceutical excipients using deep convolutional neural networks with transfer learning.

ABSTRACT: Convolutional Neural Networks (CNNs) are image analysis techniques that have been applied to image classification in various fields. In this study, we applied a CNN to classify scanning electron microscopy (SEM) images of pharmaceutical raw material powders to determine if a CNN can evaluate particle morphology. We tested 10 pharmaceutical excipients with widely different particle morphologies. SEM images for each excipient were acquired and divided into training, validation, and test sets. Classification models were constructed by applying transfer learning to pretrained CNN models such as VGG16 and ResNet50. The results of a 5-fold cross-validation showed that the classification accuracy of the CNN model was sufficiently high using either pretrained model and that the type of excipient c

SUBMITTER: Iwata H 

PROVIDER: S-EPMC9619299 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature