Information Package / Course Catalogue
Biomedical Image Processing and Machine Learning
Course Code: CSE434
Course Type: Area Elective
Couse Group: First Cycle (Bachelor's Degree)
Education Language: English
Work Placement: N/A
Theory: 2
Prt.: 2
Credit: 3
Lab: 0
ECTS: 6
Objectives of the Course

The aim of this course is to introduce the concepts of biomedical imaging systems, image processing and biomedical machine learning and to have students apply them. At the end of the course, the student will recognize biomedical images such as tomography and MR, analyze the images from these devices and learn about applying machine learning to these images.

Course Content

Basic concepts, Biomedical imaging device types, Biomedical images, image processing, Feature extraction and dimension reduction, Biomedical image classification and segmentation applications. Deep Learning on Biomedical Images.

Name of Lecturer(s)
Assoc. Prof. Ahmet Çağdaş SEÇKİN
Prof. Mehmet BİLGEN
Learning Outcomes
1.Recognizing fundamental biomedical images and datasets
2.Ability to process biomedical images
3.Image filtering and feature extraction
4.To be able to biomedical image classification and segmentation
5.To be able to use deep Learning to biomedical images
Recommended or Required Reading
1.Reyes-Aldasoro, C. C. (2015). Biomedical image analysis recipes in MATLAB: for life scientists and engineers. John Wiley & Sons.
2.Nisha, S. S., & Meeral, M. N. (2021). Applications of deep learning in biomedical engineering. In Handbook of Deep Learning in Biomedical Engineering (pp. 245-270). Academic Press.
3.Verma, S., & Agrawal, R. (2021). Deep neural network in medical image processing. In Handbook of Deep Learning in Biomedical Engineering (pp. 271-292). Academic Press.
Weekly Detailed Course Contents
Week 1 - Theoretical & Practice
BIOMEDICAL IMAGES, BASIC CONCEPTS AND , DATASETS
Week 2 - Theoretical & Practice
IMAGE PROCESSING FUNDAMENTALS
Week 3 - Theoretical & Practice
IMAGE PROCESSING FILTERS
Week 4 - Theoretical & Practice
IMAGE PROCESSING LABELING AND ANNOTATION
Week 5 - Theoretical & Practice
BIOMEDICAL IMAGE ANALYSIS-CLASSIFICATION
Week 6 - Theoretical & Practice
BIOMEDICAL IMAGE ANALYSIS-SEGMENTATION
Week 7 - Theoretical & Practice
BIOMEDICAL IMAGE CLASSIFICATION CONCEPTS
Week 8 - Theoretical & Practice
BIOMEDICAL IMAGE CLASSIFICATION CONCEPTS
Week 9 - Theoretical & Practice
BIOMEDICAL IMAGE SEGMENTATION CONCEPTS
Week 10 - Theoretical & Practice
BIOMEDICAL IMAGE CLASSIFICATION WITH MACHINE LEARNING
Week 11 - Theoretical & Practice
BIOMEDICAL IMAGE SEGMENTATION WITH MACHINE LEARNING
Week 12 - Theoretical & Practice
DEEP LEARNING BASICS
Week 13 - Theoretical & Practice
BIOMEDICAL IMAGE CLASSIFICATION WITH DNN
Week 14 - Theoretical & Practice
BIOMEDICAL IMAGE SEGMENTATION WITH DNN
Assessment Methods and Criteria
Type of AssessmentCountPercent
Final Examination1%60
Term Assignment1%40
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory140228
Lecture - Practice140228
Assignment140114
Term Project116824
Midterm Examination116824
Final Examination1161632
TOTAL WORKLOAD (hours)150
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
PÇ-8
PÇ-9
PÇ-10
PÇ-11
OÇ-1
5
4
4
4
3
OÇ-2
4
5
5
5
5
OÇ-3
2
3
3
5
5
OÇ-4
4
4
4
4
5
OÇ-5
3
2
3
3
4
Adnan Menderes University - Information Package / Course Catalogue
2026