Please use this identifier to cite or link to this item:
https://hdl.handle.net/10216/125843| Author(s): | Ana Filipa Teixeira Sampaio |
| Title: | DL4Malaria: Deep Learning Approaches for the Automated Detection and Characterisation of Malaria Parasites on Thin Blood Smear Images |
| Issue Date: | 2019-07-17 |
| Subject: | Engenharia electrotécnica, electrónica e informática Electrical engineering, Electronic engineering, Information engineering |
| Scientific areas: | Ciências da engenharia e tecnologias::Engenharia electrotécnica, electrónica e informática Engineering and technology::Electrical engineering, Electronic engineering, Information engineering |
| DOI: | 10.34626/3j6w-a440 |
| TID identifier: | 202389014 |
| URI: | https://hdl.handle.net/10216/125843 |
| Document Type: | Dissertação |
| Rights: | openAccess |
| Appears in Collections: | FEUP - Dissertação |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 344645.pdf | DLS4Malaria: statistical comparison of different deep learning segmentation approaches in microscopic thin blood sample images of Malaria | 3.99 MB | Adobe PDF |  View/Open |
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