# Transfer Learning Analysis for Subvisible Particle Flow Imaging of Pharmaceutical Formulations
## 1 Introduction
Convolutional neural networks (CNNs) are believed to learn the optimal features that can be used to automatically characterize an image from the original image [17]. CNNs have advanced the analysis of macroscopic image classifications. With supervised learning and massive image datasets, CNNs have revolutionized many practical applications of image analysis. In recent years, the accuracy of automatic image classification based on CNNs has improved with the advancement of algorithms, the performance of graphics processors, and the proliferation of datasets in various fields [18]. In some application scenarios, such as breast cancer identification [19], the performance of CNNs has matched or even surpassed that of human eye recognition. These advances have enabled efficient and robust construction of deep CNNs with multiple implicit layers. Deep CNNs can use large amounts of data to learn the optimal features automatically to characterize an image in each classification task. This approach makes complete use of image grayscale information compared to using conventional morphological features. FIM combined with CNN research has also been increasingly popular in recent years. CNN has not only obtained excellent classification results in plankton above 50 microns in size [20], but also showed potential in the subvisible particles of pharmaceutical formulations. Current research has focused on the classification of protein particle stress sources [21; 22] and the classification between proteins and silicone oils [23; 24], while ignoring other extrinsic and intrinsic particles that may also cause negative effects. Our study achieves the classification of seven classes of particles, which comprise inherent, extrinsic and intrinsic particles. This demonstrates the feasibility of FIM combined with CNN methods to achieve particle classification among multiple materials and types. In addition, because of the heterogeneity of protein particles, protein stress classification often relies on complex feature extraction methods or classification strategies, and thus it is not conducive for use in varied real process conditions. In contrast, the features learned from the ImageNet dataset can be regarded as a generic model for the visual world and applied in various fields, regardless of whether the task is relevant to ImageNet's or not [25]. This study uses an ImageNet-pre-trained convolutional base plus a custom classifier for transfer learning, and the features learned from ImageNet can be applied to particle classification after fine-tuning of the classifier. Unlike the deep learning methods in other studies, the transfer learning used in this study converges faster, requires a smaller dataset, and does not require feature extraction from scratch. Therefore, transfer learning is expected to be applied as a simple, fast and general method for subvisible particle classification in real process conditions. This study proposes a pre-trained CNN-based particle classification method to identify subvisible particles (5-80 \(\upmu\)m) commonly found in pharmaceutical protein formulations containing protein particles, cap fiber particles, glass particles, silicone oil microdroplets, silicone tubing particles, rubber closure particles, and polystyrene beads. Specifically, we prepared seven classes of standard particles of different sizes, imaged the standard samples in combination with FlowCam, then segmented and resized the images. The image set was used as the dataset of the neural network, which was then trained and validated on ImageNet-pre-trained VGG-16 to differentiate the seven classes of particles. This study demonstrates the potential of CNNs in classifying pharmaceutical formulation particles at the single-particle image level to achieve the traceability of particles.