This chapter proposes a novel hierarchical classification system based on the K-Nearest Neighbors (K-NN) model and its application to nonmelanoma skin lesion classification. Color and texture features are extracted from skin lesion images. The hierarchical structure decomposes the classification task into a set of simpler problems, one at each node of the classification. Feature selection is em...

Noncarious cervical lesions often present with overlapping symptoms and have a multifactorial etiology. The dimensions of the lesion axially (depth) and occlusogingivally (width), along with the orientation of the walls of the lesion to each other (angle), are the 3 factors that can be quantified to classify the lesion and assess its progress over time. This article analyzes these factors and p...

Convolutional Neural Networks have demonstrated human-level performance in the classification of melanoma and other skin lesions, but evident disparities between differing tones should be addressed before widespread deployment. In this work, we propose an efficient yet effective algorithm for automatically labelling tone lesion images, use to annotate benchmark ISIC dataset. We subsequently the...