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Prostate Cancer Classification using Convolutional Neural Networks

Gummeson, Anna LU (2016) In Master's Theses in Mathematical Sciences FMA820 20161
Mathematics (Faculty of Engineering)
Abstract
In 2012 prostate cancer was the second most common cancer diagnose for men. The diagnosis is confirmed by pathologists doing ocular inspection of prostate biopsies and the specimens are classified according to the Gleason grading system. The main goal of this thesis is to automate the classification using Convolutional Neural Networks (CNN).

With the introduction of Convolutional Neural Networks the field of pattern recognition broadened. The classical way of designing and extracting hand-made features for classification is substantially different to letting the computer itself decide which features are of importance, the new approach was enabled by CNNs. This together with groundbreaking results on benchmark image sets has made CNNs a... (More)
In 2012 prostate cancer was the second most common cancer diagnose for men. The diagnosis is confirmed by pathologists doing ocular inspection of prostate biopsies and the specimens are classified according to the Gleason grading system. The main goal of this thesis is to automate the classification using Convolutional Neural Networks (CNN).

With the introduction of Convolutional Neural Networks the field of pattern recognition broadened. The classical way of designing and extracting hand-made features for classification is substantially different to letting the computer itself decide which features are of importance, the new approach was enabled by CNNs. This together with groundbreaking results on benchmark image sets has made CNNs a well-used method in pattern recognition.

In this thesis a CNN with small convolutional filters has been trained from scratch using stochastic gradient descent with momentum. The error rate for the CNN is 7.3%, which is significantly better than previous works using the same data set. Since good results were obtained even though the data set were rather small, the conclusion is that CNNs are a promising method for this problem. (Less)
Please use this url to cite or link to this publication:
author
Gummeson, Anna LU
supervisor
organization
course
FMA820 20161
year
type
H2 - Master's Degree (Two Years)
subject
keywords
automated Gleason grading, prostate cancer classification, deep learning, Convolutional Neural Networks, Artificial Neural Networks, CNN, ANN
publication/series
Master's Theses in Mathematical Sciences
report number
LUTFMA-3302-2016
ISSN
1404-6342
other publication id
2016:E40
language
English
id
8891651
date added to LUP
2016-09-14 12:32:41
date last changed
2016-09-14 12:32:41
@misc{8891651,
  abstract     = {In 2012 prostate cancer was the second most common cancer diagnose for men. The diagnosis is confirmed by pathologists doing ocular inspection of prostate biopsies and the specimens are classified according to the Gleason grading system. The main goal of this thesis is to automate the classification using Convolutional Neural Networks (CNN).

With the introduction of Convolutional Neural Networks the field of pattern recognition broadened. The classical way of designing and extracting hand-made features for classification is substantially different to letting the computer itself decide which features are of importance, the new approach was enabled by CNNs. This together with groundbreaking results on benchmark image sets has made CNNs a well-used method in pattern recognition.

In this thesis a CNN with small convolutional filters has been trained from scratch using stochastic gradient descent with momentum. The error rate for the CNN is 7.3%, which is significantly better than previous works using the same data set. Since good results were obtained even though the data set were rather small, the conclusion is that CNNs are a promising method for this problem.},
  author       = {Gummeson, Anna},
  issn         = {1404-6342},
  keyword      = {automated Gleason grading,prostate cancer classification,deep learning,Convolutional Neural Networks,Artificial Neural Networks,CNN,ANN},
  language     = {eng},
  note         = {Student Paper},
  series       = {Master's Theses in Mathematical Sciences},
  title        = {Prostate Cancer Classification using Convolutional Neural Networks},
  year         = {2016},
}