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

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 could be classified with high accuracy.

The results suggest that the CNN model can detect differences in particle morphology, such as particle size, shape, and surface condition. By applying Grad-CAM to the constructed CNN model, we succeeded in finding particularly important regions in the particle image of the excipients. CNNs have been found to have the potential to be applied to the identification and characterization of raw material powders for pharmaceutical development.

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Materials

Ten types of excipients, namely, microcrystalline cellulose (CEOLUS KG-802, KG-1000, PH-101, and PH-302, Asahi Kasei Chemicals Co. Ltd., Tokyo, Japan), lactose monohydrate (Pharmatose 100 M, 125 M, and 200 M, DFE Pharma, Goch, Germany), mannitol (Mannit Q, Mitsubishi Life Science Institute, Tokyo, Japan; PEARLITOL 200SD, Roquette, Lestrem, France), and cornstarch (dried corn starch, Matsutani Chemical Industry Co., Ltd., Hyogo, Japan), were purchased commercially.

These excipients are widely used in the pharmaceutical development of oral solid dosage forms. Microcrystalline cellulose is one of the most important tableting excipients thanks to its outstanding dry binding properties, enabling the manufacture of tablets by direct compression (Thoorens et al., 2014). Mannitol and lactose are brittle excipients frequently used as a filler (Janssen et al., 2021; Paul et al., 2018). Corn starch is used as a disintegrant (Desai et al., 2016).

Hiroaki Iwata, Yoshihiro Hayashi, Aki Hasegawa, Kei Terayama, Yasushi Okuno,
Classification of scanning electron microscope images of pharmaceutical excipients using deep convolutional neural networks with transfer learning, International Journal of Pharmaceutics: X, 2022, 100135, ISSN 2590-1567,
https://doi.org/10.1016/j.ijpx.2022.100135.