# Transfer Learning Analysis for Subvisible Particle Flow Imaging of Pharmaceutical Formulations
## 4 Conclusions  
This study applied VGG16 combined with high-resolution FlowCam images based on pre-trained ImageNet datasets for subvisible particle classification. Seven typical subvisible particles (extrinsic, intrinsic, and inherent) in standard solutions of water-based particles were manually prepared to simulate the particles observed during the production of injectableotherapeutic pharmaceutical formulations. Based on the image information obtained from FlowCam, we built a dataset and trained our self-defined classifier on the convolutional base of VGG16 by pre-training the ImageNet dataset. For the prepared images of standard particles above 5 \(\upmu\)m, seven subvisible particles were predicted using the classifier described in this study. The test dataset achieved a prediction accuracy of 97.51% and more than 91% for each class of particles. In addition to accuracy, other evaluation metrics, such as recall, precision, ROC curve, and AUC, helped support the accuracy of the model. We also verified the robustness of the model against other particles in the same vial and particles in other vials under the same stress conditions using protein particles, and compared this with the machine learning method and the pre-trained VGG16. The results show that the pre-trained VGG16 was able to improve in accuracy by over 9.74% compared to the machine learning method. Pre-trained VGG16 requires less training time and is better adapted to the actual dataset collection compared to a CNN trained from scratch, and also demonstrates the possibility of applying the features learned from the ImageNet dataset to classify subvisible particles. In summary, the application of transfer learning to FlowCam image analysis to identify subvisible particles provided a classification model with high prediction accuracy for images of standard particles. To improve the practical application of the model, further efforts should be made to extend the width of the model by preparing different particle samples with different materials, composites, or stress methods. By combining the ability of CNN for sub-category recognition, this study also has the potential to combine protein stress source classification [21] to achieve identification from particle type to stress source. Through these efforts, the optimized approach has the potential to facilitate the identification of subvisible particles in industrial processes, thereby increasing their practical value while improving accuracy. This may help in the tracing of particles, ultimately shortening the investigation of root causes and advancing process improvement and control. Substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work: [PERSON], [PERSON], [PERSON] and [PERSON]; drafting the work or revising it critically for important intellectual content: [PERSON], [PERSON], L.C., [PERSON], L.M., D.H. and J.M. ; final approval of the version to be published: [PERSON], [PERSON] and [PERSON]; agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved: [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript. This work was financially supported by the National Key R&D Program of China (2021 YFF0502900), National Natural Science Foundation of China (62175034, 62175036, 82030106), Shanghai Natural Science Foundation (grant No. 20 ZR1405100, 20 ZR1403700), Science and Technology Research Program of Shanghai (grant No. 19 DZ2282100), Shanghai key discipline construction plan (2020-2022) (grant No. GWV-10.1-XX01), Shanghai Engineering Technology Research Center of Hair Medicine (19 DZ2250500), Medical Engineering Fund of Fudan University (yg2021-022), Pioneering Project of Academy for Engineering and Technology, Fudan University (gyy2018-001, gyy2018-002), Yantai Returned Scholars' Pioneering Park.