Ir al contenido (pulsa Retorno)

Universitat Politècnica de Catalunya

    • Català
    • Castellano
    • English
    • LoginRegisterLog in (no UPC users)
  • mailContact Us
  • world English 
    • Català
    • Castellano
    • English
  • userLogin   
      LoginRegisterLog in (no UPC users)

UPCommons. Global access to UPC knowledge

Banner header
64.019 UPC academic works
You are here:
View Item 
  •   DSpace Home
  • Treballs acadèmics
  • Màsters oficials
  • Master's degree in Telecommunications Engineering (MET)
  • View Item
  •   DSpace Home
  • Treballs acadèmics
  • Màsters oficials
  • Master's degree in Telecommunications Engineering (MET)
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Deep learning neural networks in malaria diagnosis

Thumbnail
View/Open
MEMORIA TFM - JAUME FERNANDEZ GARCIA.pdf (1,408Mb)
Share:
 
  View Usage Statistics
Cita com:
hdl:2117/93067

Show full item record
Fernàndez García, Jaume
Tutor / directorCabrera-Bean, MargaritaMés informacióMés informacióMés informació; Sayrol Clols, ElisaMés informacióMés informació
Document typeMaster thesis
Date2014-06-01
Rights accessOpen Access
Attribution-NonCommercial-NoDerivs 3.0 Spain
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain
Abstract
Malaria is a serious disease mostly spread in tropical and subtropical areas that causes 438.000 deaths per year. Current malaria diagnosis relies primarily on microscopic examination of stained blood films. This method is time consuming and prone to human error, even in experienced hands. Thus, there is a need for the development of an automatic technique that is able to detect malaria in a sensitive and unsupervised manner. Deep learning networks are a novel field that promises to have a key role in this automatic detection. In this thesis, we propose a system that collects much of the research conducted about this issue and that proposes new schemes to enhance the performance. In particular, a solution based on convolutional neural networks has shown a clear improvement of the results in the detection of malaria.
SubjectsMalaria -- Diagnosis, Neural networks (Computer science), Malària -- Diagnòstic, Xarxes neuronals (Informàtica)
DegreeMÀSTER UNIVERSITARI EN ENGINYERIA DE TELECOMUNICACIÓ (Pla 2013)
URIhttp://hdl.handle.net/2117/93067
Collections
  • Màsters oficials - Master's degree in Telecommunications Engineering (MET) [360]
Share:
 
  View Usage Statistics

Show full item record

FilesDescriptionSizeFormatView
MEMORIA TFM - JAUME FERNANDEZ GARCIA.pdf1,408MbPDFView/Open

Browse

This CollectionBy Issue DateAuthorsOther contributionsTitlesSubjectsThis repositoryCommunities & CollectionsBy Issue DateAuthorsOther contributionsTitlesSubjects

© UPC Obrir en finestra nova . Servei de Biblioteques, Publicacions i Arxius

info.biblioteques@upc.edu

  • About This Repository
  • Contact Us
  • Send Feedback
  • Privacy Settings
  • Inici de la pàgina