BMC Med Inform Decis Mak - Effective diagnosis of Alzheimer's disease by means of large margin-based methodology.

Tópicos

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Resumo

CKGROUND: Functional brain images such as Single-Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) have been widely used to guide the clinicians in the Alzheimer's Disease (AD) diagnosis. However, the subjectivity involved in their evaluation has favoured the development of Computer Aided Diagnosis (CAD) Systems.METHODS: It is proposed a novel combination of feature extraction techniques to improve the diagnosis of AD. Firstly, Regions of Interest (ROIs) are selected by means of a t-test carried out on 3D Normalised Mean Square Error (NMSE) features restricted to be located within a predefined brain activation mask. In order to address the small sample-size problem, the dimension of the feature space was further reduced by: Large Margin Nearest Neighbours using a rectangular matrix (LMNN-RECT), Principal Component Analysis (PCA) or Partial Least Squares (PLS) (the two latter also analysed with a LMNN transformation). Regarding the classifiers, kernel Support Vector Machines (SVMs) and LMNN using Euclidean, Mahalanobis and Energy-based metrics were compared.RESULTS: Several experiments were conducted in order to evaluate the proposed LMNN-based feature extraction algorithms and its benefits as: i) linear transformation of the PLS or PCA reduced data, ii) feature reduction technique, and iii) classifier (with Euclidean, Mahalanobis or Energy-based methodology). The system was evaluated by means of k-fold cross-validation yielding accuracy, sensitivity and specificity values of 92.78%, 91.07% and 95.12% (for SPECT) and 90.67%, 88% and 93.33% (for PET), respectively, when a NMSE-PLS-LMNN feature extraction method was used in combination with a SVM classifier, thus outperforming recently reported baseline methods.CONCLUSIONS: All the proposed methods turned out to be a valid solution for the presented problem. One of the advances is the robustness of the LMNN algorithm that not only provides higher separation rate between the classes but it also makes (in combination with NMSE and PLS) this rate variation more stable. In addition, their generalization ability is another advance since several experiments were performed on two image modalities (SPECT and PET).

Resumo Limpo

ckground function brain imag singlephoton emiss comput tomographi spect positron emiss tomographi pet wide use guid clinician alzheim diseas ad diagnosi howev subject involv evalu favour develop comput aid diagnosi cad systemsmethod propos novel combin featur extract techniqu improv diagnosi ad first region interest roi select mean ttest carri d normalis mean squar error nmse featur restrict locat within predefin brain activ mask order address small samples problem dimens featur space reduc larg margin nearest neighbour use rectangular matrix lmnnrect princip compon analysi pca partial least squar pls two latter also analys lmnn transform regard classifi kernel support vector machin svms lmnn use euclidean mahalanobi energybas metric comparedresult sever experi conduct order evalu propos lmnnbase featur extract algorithm benefit linear transform pls pca reduc data ii featur reduct techniqu iii classifi euclidean mahalanobi energybas methodolog system evalu mean kfold crossvalid yield accuraci sensit specif valu spect pet respect nmseplslmnn featur extract method use combin svm classifi thus outperform recent report baselin methodsconclus propos method turn valid solut present problem one advanc robust lmnn algorithm provid higher separ rate class also make combin nmse pls rate variat stabl addit general abil anoth advanc sinc sever experi perform two imag modal spect pet

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