2020 · 25 citations · 28 references
Skin SegmentationConvolutional Neural NetworkEngineeringFeature DetectionMachine LearningBiometricsDermatologyDeep-learned FeaturesImage ClassificationImage AnalysisPattern RecognitionDermoscopic ImageMachine VisionFeature LearningObject DetectionComputer ScienceDeep LearningMedical Image ComputingComputer VisionSkin DetectionImage Segmentation
The use of deep-learned features has recently allowed for improving the performance of skin detection and segmentation. In particular, fully-convolutional U-Nets, proposed for segmenting medical images, occurred to be extremely effective here. However, the spatial context, which is rather narrow for U-Nets, may be more important for skin segmentation than for segmenting other image structures. We propose Skinny-a lightweight U-Net-based architecture that extends the range of multi-scale analysis. The results of our experiments indicate that Skinny outperforms the state-of-the-art skin segmentation techniques, rendering the F-score of 92.3% and 94.9% for the ECU and HGR datasets, respectively.
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