Deep Learning and Computer Vision in Health (6 cr)
Code: SY00CB00-3002
General information
- Timing
-
01.08.2026 - 31.12.2026
The implementation has not yet started.
- Number of ECTS credits allocated
- 6 cr
- Local portion
- 0 cr
- Virtual proportion
- 6 cr
- Mode of delivery
- Distance learning
- Unit
- 30 Ylemmät ammattikorkeakoulututkinnot
- Campus
- Laurea Virtual Campus
- Teaching languages
- English
- Seats
- 20 - 30
- Degree programmes
- Degree Programme in Managing Digital Transformation in the Health Sector (NYD1), Laurea Virtual Campus
- Degree Programme in Managing Digital Transformation in the Health Sector (SYD1), Laurea Virtual Campus
- Teachers
- Mitha Jose
- Teacher in charge
- Mitha Jose
- Groups
-
SYD126KJDegree Programme in Managing Digital Transformation in the Health Sector, yamk, virtual studies, K26, Virtual Campus
-
NYD126KJDegree Programme in Managing Digital Transformation in the Health Sector, yamk, virtual studies, K26, Virtual Campus
- Study unit
- SY00CB00
Learning outcomes
The student is able to
- Identify an image's representation across various color spaces within the frequency domain and execute common image processing operations.
- Extract and analyze fundamental features from an image, design a convolutional neural network (CNN) architecture, and create an automated learning system employing traditional algorithms for image content classification.
- Develop comprehension of the standard architecture of a convolutional neural network (CNN) and its operational principles.
- Solve a moderately intricate image classification problem involving deep learning algorithms for the identification of image objects. Utilize transfer learning or fine-tuning techniques based on pre-trained CNNs.
- Apply deep learning algorithms to identify image objects and autonomously generate multimedia content in healthcare applications. Implement these algorithms using the open CV library and TensorFlow.
Evaluation scale
H-5