On using Support Vector Machines for the Detection and Quantification of Hand Eczema

Hand eczema is one of the most frequent skin diseases affecting up to 14% of the population. Early detection and continuous observation of eczemas allows for efficient treatment and can therefore relieve symptoms. However, purely manual skin control is tedious and often error prone. Thus, an automatic approach that can assist the dermatologist with his work is desirable. Together with our industry partner swiss4ward, we devised an image processing method for hand eczema segmentation based on support vector machines and conducted several experiments with different feature sets. Our implementation is planned to be integrated into a clinical information system for operational use at the University Hospital Zurich. Instead of focusing on a high accuracy like most existing state-of-the-art approaches, we selected F-1 score as our primary measure. This decision had several implications regarding the design of our segmentation method, since all popular implementations of support vector machines aim for optimizing accuracy. Finally, we evaluated our system and achieved an F1 score of 58.6% for front sides of hands and 43.8% for back sides, which outperforms several state-of-the-art methods that were tested on our gold standard data set as well.