An effective approach for the diagnosis of melanoma using the sparse auto-encoder for features detection and the SVM for classification.
Nadia Smaoui ZghalImene Khanfir KallelPublished in: ATSIP (2020)
Keyphrases
- feature vectors
- svm classifier
- classification method
- support vector machine svm
- support vector machine
- svm classification
- feature space
- support vector
- feature set
- classification accuracy
- skin lesion
- feature extraction
- support vector machine classifiers
- feature selection
- classification models
- classification algorithm
- input features
- feature extraction and classification
- false positives
- classification process
- histogram intersection kernel
- spam classification
- selected features
- feature reduction
- digital mammograms
- train a support vector machine
- discriminative classifiers
- classification performances
- extracted features
- benign and malignant
- generalization ability
- feature subset
- object detection
- decision forest
- training set
- decision trees
- image classification
- reduced set
- high dimension
- feature maps
- image features
- knn
- machine learning
- supervised machine learning algorithms
- class labels
- pigmented skin lesions
- malignant melanoma
- mammogram images
- pattern recognition
- training data
- high dimensional
- feature ranking
- eeg signals
- computer aided
- multiple kernel learning
- supervised learning
- video sequences
- single feature
- ensemble classifier