Accessible melanoma detection using smartphones and mobile image analysis

Thanh-Toan Do, Tuan Hoang, Victor Pomponiu, Yiren Zhou, Zhao Chen, Ngai-Man Cheung, Dawn Koh, Aaron Tan, Suat-Hoon Tan

Research output: Contribution to journalArticleResearchpeer-review

54 Citations (Scopus)

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. Our extensive evaluation on an image dataset provided by National Skin Center-Singapore (117 benign nevi and 67 malignant melanoma) confirms the effectiveness of the proposed system for melanoma detection: 89.09% sensitivity at specificity ≥90%.

Original languageEnglish
Pages (from-to)2849-2864
Number of pages16
JournalIEEE Transactions on Multimedia
Volume20
Issue number10
DOIs
Publication statusPublished - Oct 2018
Externally publishedYes

Keywords

  • feature selection
  • human-computer interface
  • malignant melan-oma (MM)
  • mobile image analysis
  • Multimedia-based healthcare

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