Melanoma Recognition with an Ensemble of Techniques for Segmentation and a Structural Analysis for Classification
This work addresses melanoma detection for medical diagnosis, but appears incremental as it combines existing techniques without clear novelty.
The paper tackles melanoma recognition by combining an ensemble segmentation approach for lesion localization with structural analysis for classification, achieving unspecified performance improvements.
An approach to lesion recognition is described that for lesion localization uses an ensemble of segmentation techniques and for lesion classification an exhaustive structural analysis. For localization, candidate regions are obtained from global thresholding of the chromatic maps and from applying the K-Means algorithm to the RGB image; the candidate regions are then integrated. For classification, a relatively exhaustive structural analysis of contours and regions is carried out.