Evaluation of state-of-the-art deep learning algorithms for multispectral skin cancer images classification
It is a present need to diagnose skin cancer sooner, faster and non-invasively. With recent achievements in the use of non-invasive imaging techniques and learning algorithms for Computer-Aided Diagnosis, the present work focuses on evaluating and adapting the different state-of-the-art artificial i...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/363291 |
| Acceso en línea: | https://hdl.handle.net/2117/363291 |
| Access Level: | acceso abierto |
| Palabra clave: | Machine learning Skin--Cancer Multispectral imaging multispectral imaging machine learning cnn skin cancer Aprenentatge automàtic Pell--Càncer Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo |
| Sumario: | It is a present need to diagnose skin cancer sooner, faster and non-invasively. With recent achievements in the use of non-invasive imaging techniques and learning algorithms for Computer-Aided Diagnosis, the present work focuses on evaluating and adapting the different state-of-the-art artificial intelligence algorithms to classify a set of multispectral images of skin lesions acquired in +400 patients with a multispectral system that works in the visible and near infrared range. It is intended that in the future, biopsies, which are the gold standard to identify equivocal lesions, will be carried out in a very unusual way |
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