Avaliação de classificadores na classificação de radiografias de tórax para o diagnóstico de pneumonia infantil
Gorde:
| Egile nagusia: | |
|---|---|
| Argitaratze data: | 2013 |
| Formatua: | Master thesis |
| Hizkuntza: | por |
| Baliabidea: | Repositório Institucional da UFG |
| Download full: | http://repositorio.bc.ufg.br/tede/handle/tede/3356 |
Gaia: | This work extends a Computer-Aided Diagnosis system called PneumoCAD for detecting pneumonia in infants using radiographic images, with the aim of improving the system’s accuracy, robustness and test the features previously extracted. We implement and compare five contemporary machine learning classifiers, namely: Naïve Bayes, K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Multi-Layer Perceptron (MLP) and Decision Tree, combined with three dimensionality reduction algorithms: the feature selection wrapper Sequential Forward Elimination (SFE), and two feature filter algotithms: Principal Component Analysis (PCA) and Kernel Principal Component Analysis (KPCA). Current Results of demonstrate that the Naïve Bayes classifier combined with KPCA produces the best overall results. Also confirming the efficiency os features. |