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Mobile Health Technologies pp 459–496Cite as

Melanoma and Other Skin Lesion Detection Using Smart Handheld Devices

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 1256))

Abstract

Smartphones of the latest generation featuring advanced multicore processors, dedicated microchips for graphics, high-resolution cameras, and innovative operating systems provide a portable platform for running sophisticated medical screening software and delivering point-of-care patient diagnostic services at a very low cost. In this chapter, we present a smartphone digital dermoscopy application that can analyze high-resolution images of skin lesions and provide the user with feedback about the likelihood of malignancy. The same basic procedure has been adapted to evaluate other skin lesions, such as the flesh-eating bacterial disease known as Buruli ulcer. When implemented on the iPhone, the accuracy and speed achieved by this application are comparable to that of a desktop computer, demonstrating that smartphone applications can combine portability and low cost with high performance. Thus, smartphone-based systems can be used as assistive devices by primary care physicians during routine office visits, and they can have a significant impact in underserved areas and in developing countries, where health-care infrastructure is limited.

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Notes

  1. 1.

    IEEE-EMBS Special Topics Conference on Point-of-Care Healthcare Technologies, Bangalore, India, 2013, http://pocht.embs.org/2013/

  2. 2.

    The RGB color space uses separate channels for red, green, and blue color intensity, HSV uses separate channels for hue, saturation, and brightness, whereas LAB uses one channel for luminance L, and two color channels, A and B, that correspond to perceived intensity of complementary colors.

  3. 3.

    http://www.dermoscopy.org/atlas/cd-review.asp

  4. 4.

    The iPhone 3G features a single core 0.6 GHz ARM Cortex processor, 0.25 GB of RAM memory, and quad core 150 MHz PowerVR SGX535 GPU with screen resolution of 164 ppi, whereas the latest model iPhone 5s features a dual core 1.3 GHz ARM Cyclone (64 bit), 1 GB of RAM memory, and quad core 300 MHz, PowerVR Series 6 Rogue G6430 GPU with screen resolution of 325 ppi (source: http://goo.gl/88YB3U).

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Acknowledgment

This research has been supported in part by NSF grant 521527 and NIH grant 5R21AR057921-02. This chapter was developed while GZ was a Visiting Professor at the Basque Center on Cognition, Brain, and Language, in Donostia-San Sebastián, Spain, and a Fellow of the Ikerbasque Foundation, Basque Country, Spain.

Author information

Authors and Affiliations

  1. Department of Engineering Technology, University of Houston, 304 Technology Building 2, Houston, TX, 77204-4020, USA

    George Zouridakis Ph.D. & Xiaojing Yuan

  2. Department of Computer Science, University of Houston, 304 Technology Building 2, Houston, TX, 77204-4020, USA

    George Zouridakis Ph.D., Tarun Wadhawan & Ning Situ

  3. Department of Electrical and Computer Engineering, University of Houston, 304 Technology Building 2, Houston, TX, 77204-4020, USA

    George Zouridakis Ph.D., Rui Hu & Keith Lancaster

  4. Department of Tropical Medicine and Global Health, Duke University, Durham, NC, 27708, USA

    Courtney M. Queen

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  1. George Zouridakis Ph.D.
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Correspondence to George Zouridakis Ph.D. .

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  1. National Cancer Institute, Rockville, Maryland, USA

    Avraham Rasooly

  2. Fischell Department of Bioengineering, University of Maryland, College Park, Maryland, USA

    Keith E. Herold

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Zouridakis, G. et al. (2015). Melanoma and Other Skin Lesion Detection Using Smart Handheld Devices. In: Rasooly, A., Herold, K. (eds) Mobile Health Technologies. Methods in Molecular Biology, vol 1256. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-2172-0_30

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  • DOI: https://doi.org/10.1007/978-1-4939-2172-0_30

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-2171-3

  • Online ISBN: 978-1-4939-2172-0

  • eBook Packages: Springer Protocols

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