Biomedical Optics Express · 2013 · 15 citations · 9 references
ComputingBiomedical EngineeringDermatologyBiomedical ImagingDiagnostic ImagingMachine LearningCancer DetectionCancer DiagnosisClassificationComputer-aided DiagnosisMedical Image AnalysisSupport Vector MachinesImbalanceMajority VotingMelanoma DiagnosisSequential Forward SelectionSpectral Band SelectionSpectral ImagingSupport Vector Machine ClassificationSynthetic Minority Oversampling
A method consisting of the combination of the Synthetic Minority Over-Sampling TEchnique (SMOTE) and the Sequential Forward Floating Selection (SFFS) technique is used to do band selection in a highly imbalanced, small size, two-class multispectral dataset of melanoma and non-melanoma lesions. The aim is to improve classification rate and help to identify those spectral bands that have a more important role in melanoma detection. All the processing steps were designed taking into account the low number of samples in the dataset, situation that is quite common in medical cases. The training/test sets are built using a Leave-One-Out strategy. SMOTE is applied in order to deal with the imbalance problem, together with the Qualified Majority Voting scheme (QMV). Support Vector Machines (SVM) is the classification method applied over each balanced set. Results indicate that all melanoma lesions are correctly classified, using a low number of bands, reaching 100% sensitivity and 72% specificity when considering nine (out of a total of 55) spectral bands.
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Corinna Cortes, Vladimir Vapnik · Machine Learning · 1995 · 33K citations · Full text
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