Unsupervised machine learning used for anomaly detection in x-ray images
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Typ
Examensarbete på kandidatnivå
Program
Publicerad
2020
Författare
Stribrand, Daniel
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
To have computers understand images has long been a hard task due to
the largely variable composition of data, but advancements in the field
of computer vision has given capable technologies of managing it. The
primary technology is the usage of machine learning, where convolutional
neural networks has shown to be really potent. This project has tackled
the task of detecting minuscule cavities in massive x-ray images of welded
metal. The challenge lies both within processing and providing data so
that it favors the machine learning model, but also developing models that
are complex enough so that it yields a general understanding of the underlying data structure. This project utilizes the benefits of unsupervised
machine learning by using a convolutional autoencoder that reconstructs
inputs through encoding and decoding. The reconstruction error of the
autoencoder is here the primary measurement for anomaly detection. By
having the autoencoder trained on millions of small, non-faulty fragments
of the x-ray images, the reconstruction error should be larger for fragments
with faulty anomalies than those without. Various statistical methodologies have been evaluated in order to find the most suitable alternative
to use on the reconstruction error. While there still is work to be done,
worthwhile achievements has been made.
Beskrivning
Ämne/nyckelord
machine learning , computer vision , anomaly detection , autoencoders , convolutional neural networks , image analysis , unsupervised learning