Accessible Melanoma Detection Using Smartphones and Mobile Image Analysis

• Published in: IEEE transactions on multimedia Vol. 20; no. 10; pp. 2849 - 2864
• Main Authors: Do, Thanh-Toan, Hoang, Tuan, Pomponiu, Victor, Zhou, Yiren, Chen, Zhao, Cheung, Ngai-Man, Koh, Dawn, Tan, Aaron, Tan, Suat-Hoon
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.10.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acceptance; Computer memory; Feature extraction; feature selection; human-computer interface; Image acquisition; Image analysis; Image color analysis; Image detection; Image segmentation; Lesions; Lightweight; malignant melan-oma (MM); Malignant tumors; Melanoma; mobile image analysis; Multimedia-based healthcare; Skin; Skin cancer; Smart phones; Smartphones; Weight reduction
• ISSN: 1520-9210; 1941-0077
• DOI: 10.1109/TMM.2018.2814346

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 <inline-formula> <tex-math notation="LaTeX">\geq</tex-math></inline-formula>90%.