| dc.contributor |
Balocco, Simone |
|
| dc.creator |
Moral Algaba, Fernando |
|
| dc.date |
2018-02-13T09:57:45Z |
|
| dc.date |
2018-02-13T09:57:45Z |
|
| dc.date |
2017-06-21 |
|
| dc.date.accessioned |
2024-12-16T10:26:12Z |
|
| dc.date.available |
2024-12-16T10:26:12Z |
|
| dc.identifier |
http://hdl.handle.net/2445/119781 |
|
| dc.identifier.uri |
http://fima-docencia.ub.edu:8080/xmlui/handle/123456789/20676 |
|
| dc.description |
Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2017, Director: Simone Balocco |
|
| dc.description |
[en] Skin cancer is the more common type of cancer. Melanoma, that begins at melanocytes, is the most aggressive type of skin cancer and responsible of about 90 % of total deaths caused by this disease. Early diagnosis is the best way to defeat melanoma and can increase survival rate to near 100 %.
Studies on Automated image detection of skin lesion has evolved achieving high rates of accuracy on melanoma detection and classification.
Deep learning and Fully Convolutional Networks has become and useful tool on image analysis. This project explores the application of FCNs on semantic segmentation over combinations of two major datasets, images from dermatologic databases and skin mole images captured by cellular phone camera.
Trained nets has been tested over another two datasets of unseen images of skin moles and dermatologic images. Data generated at this study evidence high accuracy, precision, sensitivity and speci city rates despite the small database size, which is composed by only a few hundreds images. |
|
| dc.format |
51 p. |
|
| dc.format |
application/pdf |
|
| dc.language |
spa |
|
| dc.rights |
memòria: cc-by-nc-sa (c) Fernando Moral Algaba, 2017 |
|
| dc.rights |
codi: GPL (c) Fernando Moral Algaba, 2017 |
|
| dc.rights |
http://creativecommons.org/licenses/by-sa/3.0/es |
|
| dc.rights |
http://www.gnu.org/licenses/gpl-3.0.ca.html |
|
| dc.rights |
info:eu-repo/semantics/openAccess |
|
| dc.source |
Treballs Finals de Grau (TFG) - Enginyeria Informàtica |
|
| dc.subject |
Melanoma |
|
| dc.subject |
Càncer de pell |
|
| dc.subject |
Programari |
|
| dc.subject |
Treballs de fi de grau |
|
| dc.subject |
Diagnòstic per la imatge |
|
| dc.subject |
Aprenentatge automàtic |
|
| dc.subject |
Xarxes neuronals (Informàtica) |
|
| dc.subject |
Reconeixement de formes (Informàtica) |
|
| dc.subject |
Melanoma |
|
| dc.subject |
Skin cancer |
|
| dc.subject |
Computer software |
|
| dc.subject |
Bachelor's theses |
|
| dc.subject |
Diagnostic imaging |
|
| dc.subject |
Machine learning |
|
| dc.subject |
Neural networks (Computer science) |
|
| dc.subject |
Pattern recognition systems |
|
| dc.title |
Redes completamente convolucionales en la segmentación semántica de lesiones melanocíticas |
|
| dc.type |
info:eu-repo/semantics/bachelorThesis |
|