Prostate Cancer Classification using Convolutional Neural Networks
(2016) In Master's Theses in Mathematical Sciences FMA820 20161Mathematics (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:
http://lup.lub.lu.se/student-papers/record/8891651
- author
- Gummeson, Anna LU
- supervisor
-
- Karl Åström LU
- Mattias Ohlsson LU
- organization
- course
- FMA820 20161
- year
- 2016
- 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}}, language = {{eng}}, note = {{Student Paper}}, series = {{Master's Theses in Mathematical Sciences}}, title = {{Prostate Cancer Classification using Convolutional Neural Networks}}, year = {{2016}}, }