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Classification in Bone Scintigraphy Images Using Convolutional Neural Networks

Dang, Johnny LU (2016) In Master's Theses in Mathematical Sciences FMA820 20161
Mathematics (Faculty of Engineering)
Abstract
The main goal of this thesis is to design and implement a convolutional neural network (CNN) to classify whether hotspots in bone scintigraphy images represent cancer metastases, caused by prostate cancer, or some other physiological process. A side task was included, where another CNN was designed and trained to classify whether a bone scintigraphy image image is acquired from the front of the patient (anterior) or from the back (posterior). The CNNs were implemented using the deep learning frameworks Deeplearning4J and Keras, and were trained on labelled data sets provided by Exini Diagnostics AB, Lund, Sweden.

The performance of the CNNs were evaluated using various metrics commonly used in machine learning contexts, for instance... (More)
The main goal of this thesis is to design and implement a convolutional neural network (CNN) to classify whether hotspots in bone scintigraphy images represent cancer metastases, caused by prostate cancer, or some other physiological process. A side task was included, where another CNN was designed and trained to classify whether a bone scintigraphy image image is acquired from the front of the patient (anterior) or from the back (posterior). The CNNs were implemented using the deep learning frameworks Deeplearning4J and Keras, and were trained on labelled data sets provided by Exini Diagnostics AB, Lund, Sweden.

The performance of the CNNs were evaluated using various metrics commonly used in machine learning contexts, for instance accuracy and Receiver Operating Characteristics. The final trained CNN used for the classification
of hotspots reached an accuracy of 89% on a test set consisting of images
that had not been used for training. The corresponding CNN for the side
task reached an accuracy of 99%. These results indicate that CNNs can work
well for both classification problems. (Less)
Popular Abstract (Swedish)
Skelettskintigrafi är en bildgivande undersökning liknande röntgen, där cancer metastaser och andra benskador visar sig som hotspots. Att identifiera dessa hotspots är en svår uppgift även för erfarna läkare. I detta arbete används faltningsbaserade neuronnät till att träna datorer att hjälpa läkare identifiera hotspots.
Please use this url to cite or link to this publication:
author
Dang, Johnny LU
supervisor
organization
alternative title
Applicering av Neuronnät på Bone Scans
course
FMA820 20161
year
type
H2 - Master's Degree (Two Years)
subject
keywords
machine learning, deep learning, neural networks, convolutional neural networks, artificial neural network, ANN, CNN, bone scintigraphy, bone scan, prostate cancer, bone metastasis, bone scan index
publication/series
Master's Theses in Mathematical Sciences
report number
LUTFMA-3297-2016
ISSN
1404-6342
other publication id
2016:E24
language
English
id
8881492
date added to LUP
2016-08-25 13:29:09
date last changed
2024-10-03 13:12:27
@misc{8881492,
  abstract     = {{The main goal of this thesis is to design and implement a convolutional neural network (CNN) to classify whether hotspots in bone scintigraphy images represent cancer metastases, caused by prostate cancer, or some other physiological process. A side task was included, where another CNN was designed and trained to classify whether a bone scintigraphy image image is acquired from the front of the patient (anterior) or from the back (posterior). The CNNs were implemented using the deep learning frameworks Deeplearning4J and Keras, and were trained on labelled data sets provided by Exini Diagnostics AB, Lund, Sweden.

The performance of the CNNs were evaluated using various metrics commonly used in machine learning contexts, for instance accuracy and Receiver Operating Characteristics. The final trained CNN used for the classification
of hotspots reached an accuracy of 89% on a test set consisting of images
that had not been used for training. The corresponding CNN for the side
task reached an accuracy of 99%. These results indicate that CNNs can work
well for both classification problems.}},
  author       = {{Dang, Johnny}},
  issn         = {{1404-6342}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Master's Theses in Mathematical Sciences}},
  title        = {{Classification in Bone Scintigraphy Images Using Convolutional Neural Networks}},
  year         = {{2016}},
}