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Rzeszów University of Technology
Electrical and Computer Engineering
The Faculty of:
Field of study:
Technical Physics
Speciality:
Physics in Medicine and Technology
Study degree (BSc, MSc):
MSc
COURSE UNIT DESCRIPTION
Artificial Intelligence in Medical Diagnostics
Course title:
Lecturer responsible for course: prof. Jacek Kluska
Contacts: phone: +48 17 8651247
e-mail: [email protected]
Department : Computer Science and Automatic Control
Type of classes
Semester
Weekly load
2
4
L
Lectures
C
Theoretical
Classes
Lb
Laboratory
P
Project
15
15
30
Number of
ECTS
credits
3
Course description
Lecture:
Description of uncertainty. Fuziness vs. probability. Knowledge-bases and inference methods. Generalized
expert system design. Artificial neural networks. Classification. Linear separability. Convergence of the
perceptron learning. Multilayer networks. Backpropagation neural networks. Adaptive linear neuron. WienerHoff equation. Newton-Raphson algorithm. Ideal gradient descent method. Widrow-Hoff delta rule. Recursive
least squares algorithm. Selforganizing networks. Counter-propagation networks. Unsupervised learning in the
Hopfield networks. k-nearest neighbors algorithm. Naive Bayes classifier. Decision trees and families of
classifiers. Gene Expression Programming and its applications. Support Vector Machines. Optimal separating
hyperplane. Kernel functions. SMO algorithm. Medical decision support systems (ovarian cancer and breast
cancer data sets).
Classes:
Laboratory:
Comparison of SVM, k-NN and feedforward neural networks (perceptron networks, RBF, LCQ) for the medical
data sets (real data: ovarian cancer and breast cancer). Cross validation. Improved Sequential Minimal
Optimization algorithm.
Project:
Using procedures of the Matlab toolboxes. Investigation of backpropagation error algorithm. Sensistivity and
specificity. Design of the basic elements for a medical decision support system.
Objectives of the course
Goal of the course is to learn about computer systems that exhibit intelligent behavior. Topics include fuzzy
expert systems, neural networks and optimal classification methods. Practical problems concern with medical
applications.
Examination method
Written solution of design problems and oral discussion.
Bibliography
Bibliography (in english)
1. Gunn S., Support Vector Machines for Classification and Regression, University of Southampton, 1998.
2. Ferreira C., Gene expression programming. Springer-Verlag, 2006.
3. Haykin S., Neural Networks – a Comprehensive Foundation, Macmillan College Publishing Company,
New York, 1994
4. Kluska J., Analytical methods in fuzzy modeling and control. Springer-Verlag, Berlin Heidelberg 2009.
5. Kusy M., System for Cancer Diagnosis Based on Support Vector Machines and Neural Networks, Ph.D.
Thesis, Warsaw Univ. of Technology 2008.
Lecturer signature
Head of Department signature
Dean signature