dc.contributor |
Universitat Ramon Llull. La Salle |
dc.contributor |
Universitat de Girona |
dc.contributor |
Hospital Universitari Doctor Josep Trueta |
dc.contributor.author |
Golobardes, Elisabet |
dc.contributor.author |
Martí, Joan |
dc.contributor.author |
Español, Josep |
dc.contributor.author |
Salamó Llorente, Maria |
dc.contributor.author |
Freixenet, Jordi |
dc.contributor.author |
Llorà Fàbrega, Xavier |
dc.contributor.author |
Maroto, Albert |
dc.contributor.author |
Bernadó Mansilla, Ester |
dc.date.accessioned |
2021-05-07T07:18:16Z |
dc.date.available |
2021-05-07T07:18:16Z |
dc.date.created |
2001-10 |
dc.date.issued |
2001-10 |
dc.identifier.uri |
http://hdl.handle.net/2072/449949 |
dc.format.extent |
8 p. |
dc.language.iso |
eng |
dc.publisher |
4rt Congrés Català d'Intel.ligència Artificial, Barcelona, 24-25 d'octubre de 2001 |
dc.rights |
L'accés als continguts d'aquest document queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons:http://creativecommons.org/licenses/by-nc-nd/4.0/ |
dc.rights |
© ACIA |
dc.source |
RECERCAT (Dipòsit de la Recerca de Catalunya) |
dc.subject.other |
Intel·ligència artificial -- Aplicacions a la medicina |
dc.subject.other |
Aprenentatge automàtic |
dc.title |
Classifying Microcalcifications in Digital Mammograms using Machine Learning techniques |
dc.type |
info:eu-repo/semantics/conferenceObject |
dc.subject.udc |
004 - Informàtica |
dc.subject.udc |
37 - Educació. Ensenyament. Formació. Temps lliure |
dc.subject.udc |
62 - Enginyeria. Tecnologia |
dc.embargo.terms |
cap |
dc.rights.accessLevel |
info:eu-repo/semantics/openAccess |
dc.description.abstract |
This paper presents a Computer Aided Diagnosis (CAD) of breast cancer from mammograms. The first part involves severa! image processing techniques, which extract a set of features from the microcalcifications (µCa) present in a mammogram. The second part applies different machine learning techniques to obtain an automatic diagnosis. The Machine Learning (ML) approaches are: Case-Based Reasoning (CBR) and Genetic Algorithms (GA). We study the application of these algorithms as classification systems in order to differentiate benign from malignant µCa in mammograms, obtained from the mammography database of the Girona Health Area, and we compare the classification results to other classification techniques. |