BACKGROUND: @entity8721 , tiny skin vessels, are important dermoscopy structures used to discriminate @entity1491 ( @entity1491 ) from benign @entity272 . This research builds off of previously developed image analysis techniques to identify vessels automatically to discriminate benign lesions from BCCs. METHODS: A biologically inspired reinforcement learning approach is investigated in an adaptive critic design framework to apply @entity734 ( @entity734 ) for discrimination based on computed features using different @entity272 contrast variations to promote the discrimination process. Lesion discrimination results for @entity734 are compared with multilayer perception backpropagation artificial neural networks. RESULTS: This study uses a data set of 498 dermoscopy @entity272 images of 263 BCCs and 226 competitive benign images as the input sets. This data set is extended from previous research [Cheng et al., Skin Research and Technology, 2011, 17: 278]. Experimental results yielded a diagnostic accuracy as high as 84.6% using the @entity734 approach, providing an 8.03% improvement over a standard multilayer perception method. CONCLUSION: We have chosen @entity1491 detection rather than vessel detection as the endpoint. Although vessel detection is inherently easier, @entity1491 detection has potential direct clinical applications. Small BCCs are detectable early by dermoscopy and potentially detectable by the automated methods described in this research.

Automatic XXXX analysis in dermoscopy images using adaptive critic design.

@entity8721 :: ('MESH:D013684', 'Disease') :: ['Telangiectasia'] @entity734 :: ('MESH:D019966', 'Disease') :: ['ADHDP', 'action-dependent heuristic dynamic programming'] @entity1491 :: ('MESH:D002280', 'Disease') :: ['basal cell carcinoma', 'BCC'] @entity272 :: ('MESH:D012871', 'Disease') :: ['skin lesion', 'skin lesions']
@entity8721 :: (MESH:D013684,Disease) :: ['telangiectasia']
