  This short report describes our submission to the ISIC 2018 Challenge in Skin
Lesion Analysis Towards Melanoma Detection for Task1 and Task 3. This work has
been accomplished by a team of researchers at the University of Dayton Signal
and Image Processing Lab. Our proposed approach is computationally efficient
are combines information from both deep learning and handcrafted features. For
Task3, we form a new type of image features, called hybrid features, which has
stronger discrimination ability than single method features. These features are
utilized as inputs to a decision-making model that is based on a multiclass
Support Vector Machine (SVM) classifier. The proposed technique is evaluated on
online validation databases. Our score was 0.841 with SVM classifier on the
validation dataset.
