Skip to main content

Automated Mobile Image Acquisition of Skin Wounds Using Real-Time Deep Neural Networks

Part of the Communications in Computer and Information Science book series (CCIS,volume 1065)


Periodic image acquisition plays an important role in the monitoring of different skin wounds. With a visual history, health professionals have a clear register of the wound’s state at different evolution stages, which allows a better overview of the healing progress and efficiency of the therapeutics being applied. However, image quality and adequacy has to be ensured for proper clinical analysis, being its utility greatly reduced if the image is not properly focused or the wound is partially occluded. This paper presents a new methodology for automated image acquisition of skin wounds via mobile devices. The main differentiation factor is the combination of two different approaches to ensure simultaneous image quality and adequacy: real-time image focus validation; and real-time skin wound detection using Deep Neural Networks (DNN). A dataset of 457 images manually validated by a specialist was used, being the best performance achieved by a SSDLite MobileNetV2 model with mean average precision of 86.46% using 5-fold cross-validation, memory usage of 43 MB, and inference speeds of 23 ms and 119 ms for desktop and smartphone usage, respectively. Additionally, a mobile application was developed and validated through usability tests with eleven nurses, attesting the potential of using real-time DNN approaches to effectively support skin wound monitoring procedures.


  • Skin wounds
  • Mobile health
  • Object detection
  • Deep learning
  • Mobile devices

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions


  1. 1.

  2. 2.

  3. 3.


  1. Europe wound management market - segmented by product, wound healing therapy and geography - growth, trends, and forecast (2018–2023). Technical report, Mordor Intelligence, April 2018

    Google Scholar 

  2. Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The Pascal visual object classes (VOC) challenge. Int. J. Comput. Vis. 88(2), 303–338 (2010)

    CrossRef  Google Scholar 

  3. Goyal, M., Reeves, N., Rajbhandari, S., Yap, M.H.: Robust methods for real-time diabetic foot ulcer detection and localization on mobile devices. IEEE J. Biomed. Health Inf. 23, 1730–1741 (2018)

    CrossRef  Google Scholar 

  4. Howard, A.G., et al.: Mobilenets: efficient convolutional neural networks for mobile vision applications. CoRR abs/1704.04861 (2017)

    Google Scholar 

  5. Huang, J., et al.: Speed/accuracy trade-offs for modern convolutional object detectors. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3296–3297 (2018)

    Google Scholar 

  6. Järbrink, K., et al.: The humanistic and economic burden of chronic wounds: a protocol for a systematic review. Syst. Rev. 6(1), 15 (2017)

    CrossRef  Google Scholar 

  7. Li, F., Wang, C., Liu, X., Peng, Y., Jin, S.: Corrigendum to “a composite model of wound segmentation based on traditional methods and deep neural networks”. Comput. Intell. Neurosci. 2018, 1 (2018)

    Google Scholar 

  8. Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014).

    CrossRef  Google Scholar 

  9. Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016).

    CrossRef  Google Scholar 

  10. Liu, X., Wang, C., Li, F., Zhao, X., Zhu, E., Peng, Y.: A framework of wound segmentation based on deep convolutional networks. In: 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), pp. 1–7 (2017)

    Google Scholar 

  11. Medetec: Wound database (2014).

  12. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017)

    CrossRef  Google Scholar 

  13. Rosado, L., et al.: \(\mu \)SmartScope: towards a fully automated 3D-printed smartphone microscope with motorized stage. In: Peixoto, N., Silveira, M., Ali, H.H., Maciel, C., van den Broek, E.L. (eds.) BIOSTEC 2017. CCIS, vol. 881, pp. 19–44. Springer, Cham (2018).

    CrossRef  Google Scholar 

  14. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.: MobileNetV2: inverted residuals and linear bottlenecks. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)

    Google Scholar 

  15. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826, June 2016

    Google Scholar 

  16. Tenenbaum, J.M.: Accommodation in computer vision. Technical report, Stanford University California Department of Computer Science (1970)

    Google Scholar 

  17. Wang, C., et al.: A unified framework for automatic wound segmentation and analysis with deep convolutional neural networks. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2415–2418 (2015)

    Google Scholar 

Download references


This work was done under the scope of MpDS - “Medical Pre-diagnostic System” project, identified as POCI-01-0247-FEDER-024086, according to Portugal 2020 is co-funded by the European Structural Investment Funds from European Union, framed in the COMPETE 2020.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Luís Rosado .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Faria, J., Almeida, J., Vasconcelos, M.J.M., Rosado, L. (2020). Automated Mobile Image Acquisition of Skin Wounds Using Real-Time Deep Neural Networks. In: Zheng, Y., Williams, B., Chen, K. (eds) Medical Image Understanding and Analysis. MIUA 2019. Communications in Computer and Information Science, vol 1065. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-39342-7

  • Online ISBN: 978-3-030-39343-4

  • eBook Packages: Computer ScienceComputer Science (R0)