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This short report describes our submission to the ISIC 2018 Challenge in SkinLesion Analysis Towards Melanoma Detection for Task1 and Task 3. This work hasbeen accomplished by a team of researchers at the University of Dayton Signaland Image Processing Lab. Our proposed approach is computationally efficientare combines information from both deep learning and handcrafted features. ForTask3, we form a new type of image features, called hybrid features, which hasstronger discrimination ability than single method features. These features areutilized as inputs to a decision-making model that is based on a multiclassSupport Vector Machine (SVM) classifier. The proposed technique is evaluated ononline validation databases. Our score was 0.841 with SVM classifier on thevalidation dataset.

Skin Lesion Segmentation and Classification for ISIC 2018 by Combining Deep CNN and Handcrafted Features