Automated Visual Inspection of Lyophilized Products via Deep Learning and Autoencoders
Name
Tran-petpanda-meng-eecs-2021-thesis.pdf
Description
Thesis PDF
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4.31 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Tran, Peter
Advisor(s)
Boning, Duane S.
Date Issued
September 2021
Publisher
Massachusetts Institute of Technology
Abstract
Manual visual inspection of every sterile parenteral product for defects is costly, cumbersome, and inconsistent. Industry standard currently relies on a semi-automated method, but the percentage of vials that require additional human inspection is high, at 30%. Using deep learning can help reduce this percentage, but there are a number of challenges. In particular, the dataset for defective lyophilized products is not only small, but also suffers from class imbalance because of the small number of defects.
In this thesis, we test the performance of well known deep learning neural network architectures including VGG16 and ResNet50. We compare results from training these architectures from scratch to results using fine-tuning of pretrained variants of the models, and find that the pretrained variants not only help improve accuracies, but help the model learn the correct reasoning for the defect classification decision, in terms of identifying defect location in an image. Furthermore, we show that autoencoders can be used to create classifiers that perform just as well as the pretrained VGG16 and ResNet50 models for our vial image datasets. Lastly, we demonstrate that simple data augmentation techniques do not improve the training of our vial defect classification models.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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