A comparison of SVM and HMM classifiers in the off-line signature verification
Pattern Recognition Letters
Computers in Biology and Medicine
Computer Methods and Programs in Biomedicine
Texture analysis for ulcer detection in capsule endoscopy images
Image and Vision Computing
Curvelet based face recognition via dimension reduction
Signal Processing
Image compression scheme based on curvelet transform and support vector machine
Expert Systems with Applications: An International Journal
Methodology for automatic detection of lung nodules in computerized tomography images
Computer Methods and Programs in Biomedicine
Artificial Intelligence in Medicine
Sparse classification for computer aided diagnosis using learned dictionaries
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Robust large scale prone-supine polyp matching using local features: a metric learning approach
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Computer-Assisted Diagnosis of Tuberculosis: A First Order Statistical Approach to Chest Radiograph
Journal of Medical Systems
Computer Methods and Programs in Biomedicine
Automated pulmonary nodule detection based on three-dimensional shape-based feature descriptor
Computer Methods and Programs in Biomedicine
Computer Methods and Programs in Biomedicine
A new hybrid intelligent system for accurate detection of Parkinson's disease
Computer Methods and Programs in Biomedicine
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Lung cancer is one of the most common forms of cancer resulting in over a million deaths per year worldwide. In this paper, the usage of support vector machine (SVM) classification for lung cancer is investigated, presenting a systematic quantitative evaluation against Boosting, Decision trees, k-nearest neighbor, LASSO regressions, neural networks and random forests. A large database of 5984 regions of interest (ROIs) and 488 input features (including textural features, patient characteristics, and morphological features) were used to train the classifiers and evaluate for their performance. The evaluation for classifiers' performance was based on a tenfold cross validation framework, receiver operating characteristic curve (ROC), and Matthews correlation coefficient. Area under curve (AUC) of SVM, Boosting, Decision trees, k-nearest neighbor, LASSO, neural networks, random forests were 0.94, 0.86, 0.73, 0.72, 0.91, 0.92, and 0.85, respectively. It was proved that SVM classification offered significantly increased classification performance compared to the reference methods. This scheme may be used as an auxiliary tool to differentiate between benign and malignant SPNs of CT images in future