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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
SYD126KJ
Degree Programme in Managing Digital Transformation in the Health Sector, yamk, virtual studies, K26, Virtual Campus
NYD126KJ
Degree 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

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