Accessible Melanoma Detection using Smartphones and Mobile Image Analysis
This work addresses early melanoma detection for general users via smartphones, but it appears incremental as it builds on existing mobile imaging and segmentation approaches.
The paper tackles melanoma detection using smartphone-captured images under loosely-controlled conditions, proposing a system optimized for resource-constrained devices that localizes lesions with lightweight methods and new features, achieving unspecified performance metrics.
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.