Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
4TGT276Digital Technologies in Health3+0+03423.03.2026

 
Course Details
Language of Instruction Turkish
Level of Course Unit Associate Degree
Department / Program Medical Imaging Techniques
Type of Program Formal Education
Type of Course Unit Compulsory
Course Delivery Method Face To Face
Objectives of the Course The aim of this course is to provide students with the knowledge of machine learning, image reconstruction and image processing methods used in medical imaging systems and to provide the ability to apply them on medical images.
Course Content Teaches the image formation process in medical imaging systems (such as CT, MRI, PET, ultrasound) and how image processing can be done with machine learning techniques in these systems.
Course Methods and Techniques Face to face
Prerequisites and co-requisities None
Course Coordinator None
Name of Lecturers Associate Prof.Dr. İzzet Paruğ DURU
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources Digital Image Processing, Rafael C. Gonzalez, Richard E. Woods, 3rd Edition, December 2013, Palme Publishing.
The Mathematics of Medical Imaging, A Beginner’s Guide, Timothy G. Feeman, Springer.
Convolutional Neural Networks for Medical Image Processing Applications, Saban Ozturk, CRC press.
Course Notes 1- Digital Image Processing, Rafael C. Gonzalez, Richard E. Woods, 3rd Edition, December 2013, Palme Publishing.
2-The Mathematics of Medical Imaging, A Beginner’s Guide, Timothy G. Feeman, Springer.
3-Convolutional Neural Networks for Medical Image Processing Applications, Saban Ozturk, CRC press.

Course Category
Mathematics and Basic Sciences %20
Engineering %30
Health %50

Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Mid-terms 1 % 20
Assignment 1 % 30
Final examination 1 % 50
Total
3
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Course Duration 14 3 42
Hours for off-the-c.r.stud 14 3 42
Assignments 2 6 12
Mid-terms 1 2 2
Final examination 1 5 5
Total Work Load   Number of ECTS Credits 4 103

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Understand the operation and data flow of medical imaging systems.
2 Be able to apply image processing algorithms and machine learning techniques on medical images.
3 Gain skills in data preprocessing, segmentation and classification of medical images.
4 Understand the ethical responsibilities for developing artificial intelligence applications in the field of healthcare.
5 It will be able to detect diseases with Convolutional Neural Networks (CNN) and deep learning methods.

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 General introduction to medical imaging systems (CT, MRI, PET, Ultrasound)
2 Basic principles of image processing: Filtering, segmentation, edge detection
3 Use of machine learning algorithms in medical imaging
4 Image classification with Convolutional Neural Networks (CNN)
5 Deep learning methods
6 Data augmentation techniques in medical images
7 Feature extraction and dimensionality reduction for medical images.
8 Time series image analyses: Functional MRI (fMRI)
9 Artificial intelligence-based decision support systems in medical imaging
10 Ethics and Data Security in Medical Imaging
11 Mammography Applications
12 Computed Tomography (CR) Applications
13 Magnetic Resonance Imaging (MRI) Applications
14 Magnetic Resonance Imaging (MRI) Applications

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13
C1 2 2 4 1 5
C2 2 3 4 1 5
C3 2 3 4 1 5
C4
C5 2 5 2 4 1 5

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  https://obs.gedik.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=239120&curProgID=80&lang=en