dc.contributor.author |
Sharka, Vule |
|
dc.date.accessioned |
2025-01-23T13:49:27Z |
|
dc.date.available |
2025-01-23T13:49:27Z |
|
dc.date.issued |
2022-07-15 |
|
dc.identifier.uri |
http://dspace.epoka.edu.al/handle/1/2395 |
|
dc.description.abstract |
The purpose of this thesis is to create a deep learning application for
solving the problem of detection the cells in some images. Artificial intelligence is
becoming increasingly important in the field of biology, imagery, and medicine, as it
can aid in processes that are difficult to do by humans. Image analysis tasks can be
performed in a less prone to error way by introducing these algorithms, in such a way
of avoiding issues with biological variance, variations in contrast or brightness, slide
preparation, cell anomalies, arrangements, etc. In this work I am going to use U-Net
to achieve cell segmentation, by training the neural network on our dataset of cell
images. |
en_US |
dc.language.iso |
en |
en_US |
dc.subject |
U-Net architecture, nucleus, segmentation, deep-learning, artificial intelligence, neural network, train, test |
en_US |
dc.title |
CELL SEGMENTATION AND COUNTING USING U-NET ARCHITECTURE |
en_US |
dc.type |
Thesis |
en_US |