J Med Syst - Three-dimensional SVM with latent variable: application for detection of lung lesions in CT images.

Tópicos

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Resumo

The study aims to improve the performance of current computer-aided schemes for the detection of lung lesions, especially the low-contrast in gray density or irregular in shape. The relative position between suspected lesion and whole lung is, for the first time, added as a latent feature to enrich current Three-dimensional (3D) features such as shape, texture. Subsequently, 3D matrix patterns-based Support Vector Machine (SVM) with the latent variable, referred to as L-SVM3Dmatrix, was constructed accordingly. A CT image database containing 750 abnormal cases with 1050 lesions was used to train and evaluate several similar computer-aided detection (CAD) schemes: traditional features-based SVM (SVMfeature), 3D matrix patterns-based SVM (SVM3Dmatrix) and L-SVM3Dmatrix. The classifier performances were evaluated by computing the area under the ROC curve (AUC), using a 5-fold cross-validation. The L-SVM3Dmatrix sensitivity was 93.0 with 1.23% percentage of False Positive (FP), the SVM3Dmatrix sensitivity was 88.4 with 1.49% percentage of FP, and the SVMfeature sensitivity was 87.2 with 1.78% percentage of FP. The L-SVM3Dmatrix outperformed other current lung CAD schemes, especially regarding the difficult lesions.

Resumo Limpo

studi aim improv perform current computeraid scheme detect lung lesion especi lowcontrast gray densiti irregular shape relat posit suspect lesion whole lung first time ad latent featur enrich current threedimension d featur shape textur subsequ d matrix patternsbas support vector machin svm latent variabl refer lsvmdmatrix construct accord ct imag databas contain abnorm case lesion use train evalu sever similar computeraid detect cad scheme tradit featuresbas svm svmfeatur d matrix patternsbas svm svmdmatrix lsvmdmatrix classifi perform evalu comput area roc curv auc use fold crossvalid lsvmdmatrix sensit percentag fals posit fp svmdmatrix sensit percentag fp svmfeatur sensit percentag fp lsvmdmatrix outperform current lung cad scheme especi regard difficult lesion

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