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• W2770817817 abstract "We investigate the design of an entire mobile imaging system for early detection of melanoma. Different from previous work, we focus on smartphone-captured visible light images. Our design addresses two major challenges. First, images acquired using a smartphone under loosely-controlled environmental conditions may be subject to various distortions, and this makes melanoma detection more difficult. Second, processing performed on a smartphone is subject to stringent computation and memory constraints. In our work, we propose a detection system that is optimized to run entirely on the resource-constrained smartphone. Our system intends to localize the skin lesion by combining a lightweight method for skin detection with a hierarchical segmentation approach using two fast segmentation methods. Moreover, we study an extensive set of image features and propose new numerical features to characterize a skin lesion. Furthermore, we propose an improved feature selection algorithm to determine a small set of discriminative features used by the final lightweight system. In addition, we study the human-computer interface (HCI) design to understand the usability and acceptance issues of the proposed system." @default.
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• W2770817817 date "2017-11-27" @default.
• W2770817817 modified "2023-10-18" @default.
• W2770817817 title "Accessible Melanoma Detection using Smartphones and Mobile Image Analysis" @default.
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• W2770817817 doi "https://doi.org/10.48550/arxiv.1711.09553" @default.
• W2770817817 hasPublicationYear "2017" @default.
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