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http://hdl.handle.net/10609/126709
Title: | Detección y clasificación de células anormales de sangre periférica usando técnicas de N-Shot Learning |
Author: | Lucas Guerrero, José |
Tutor: | Alférez, Santiago |
Others: | Prados Carrasco, Ferran |
Abstract: | Using Machine Learning technologies for digital image processing, it is possible to develop applications able to classify images between different categories. In this master's thesis, Deep Learning models of the n-shot learning type have been implemented for the classification of peripheral blood images. These techniques are suitable when not enough sample is available for the training process. A Siamese Network has been developed for checking if two different images correspond to the same cell type. A five-shot network has been developed to classify leukocytes between healthy and affected by Burkkit's Lymphoma. Both models were developed in PyTorch and Fast.ai obtaining success rates higher than 75% and 85% respectively. The conclusion of the work is that few shot techniques are a good approach when not enough sample is available to apply conventional techniques. |
Keywords: | deep learning n-shot learning lymphoids cells lymphomas |
Document type: | info:eu-repo/semantics/masterThesis |
Issue Date: | 2-Jan-2021 |
Publication license: | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
Appears in Collections: | Trabajos finales de carrera, trabajos de investigación, etc. |
Files in This Item:
File | Description | Size | Format | |
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pepelucasTFM0121memoria.pdf | Memoria del TFM | 1,43 MB | Adobe PDF | View/Open |
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