Accessible Melanoma Detection using Smartphones and Mobile Image Analysis

TL;DR

This paper presents a mobile system for early melanoma detection using smartphone images, addressing challenges of image distortion and limited processing power, with a focus on lightweight algorithms and user interface design.

Contribution

It introduces a fully mobile, resource-efficient melanoma detection system with novel feature selection and hierarchical segmentation tailored for smartphones.

Findings

System runs entirely on smartphones with limited resources.

Proposed features improve lesion characterization accuracy.

User interface design enhances usability and acceptance.

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.