卷积神经网络
人工智能
扫描电子显微镜
赋形剂
模式识别(心理学)
粒子(生态学)
计算机科学
人工神经网络
生物系统
聚类分析
投影(关系代数)
材料科学
色谱法
化学
算法
复合材料
生物
海洋学
地质学
作者
Hiroaki Iwata,Yoshihiro Hayashi,Takuto Koyama,Aki Hasegawa,Kosuke Ohgi,Ippei Kobayashi,Yasushi Okuno
标识
DOI:10.1016/j.ijpharm.2024.123873
摘要
Scanning electron microscopy (SEM) images are the most widely used tool for evaluating particle morphology; however, quantitative evaluation using SEM images is time-consuming and often neglected. In this study, we aimed to extract features related to particle morphology of pharmaceutical excipients from SEM images using a convolutional neural network (CNN). SEM images of 67 excipients were acquired and used as models. A classification CNN model of the excipients was constructed based on the SEM images. Further, features were extracted from the middle layer of this CNN model, and the data was compressed to two dimensions using uniform manifold approximation and projection. Lastly, hierarchical clustering analysis (HCA) was performed to categorize the excipients into several clusters and identify similarities among the samples. The classification CNN model showed high accuracy, allowing each excipient to be identified with a high degree of accuracy. HCA revealed that the 67 excipients were classified into seven clusters. Additionally, the particle morphologies of excipients belonging to the same cluster were found to be very similar. These results suggest that CNN models are useful tools for extracting information and identifying similarities among the particle morphologies of excipients.
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