Leveraging deep neural networks for automatic and standardised wound image acquisition

Ana Filipa Sampaio, Pedro Alves, Nuno Cardoso, Paulo Alves, Raquel Marques, Pedro Salgado, Maria João M. Vasconcelos

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)
10 Downloads

Abstract

Wound monitoring is a time-consuming and error-prone activity performed daily by healthcare professionals. Capturing wound images is crucial in the current clinical practice, though image inadequacy can undermine further assessments. To provide sufficient information for wound analysis, the images should also contain a minimal periwound area. This work proposes an automatic wound image acquisition methodology that exploits deep learning models to guarantee compliance with the mentioned adequacy requirements, using a marker as a metric reference. A RetinaNet model detects the wound and marker regions, further analysed by a post-processing module that validates if both structures are present and verifies that a periwound radius of 4 centimetres is included. This pipeline was integrated into a mobile application that processes the camera frames and automatically acquires the image once the adequacy requirements are met. The detection model achieved mAP@.75IOU values of 0.39 and 0.95 for wound and marker detection, exhibiting a robust detection performance for varying acquisition conditions. Mobile tests demonstrated that the application is responsive, requiring 1.4 seconds on average to acquire an image. The robustness of this solution for real-time smartphone-based usage evidences its capability to standardise the acquisition of adequate wound images, providing a powerful tool for healthcare professionals.
Original languageEnglish
Title of host publicationProceedings of the 9th International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE 2023
EditorsMaria Lozano Perez, Maurice Mulvenna, Martina Ziefle
PublisherScience and Technology Publications, Lda
Pages253-261
Number of pages9
ISBN (Electronic)9789897586453
DOIs
Publication statusPublished - 2023
Event9th International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE 2023 - Prague, Czech Republic
Duration: 22 Apr 202324 Apr 2023

Publication series

NameInternational Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE - Proceedings
Volume2023-April
ISSN (Electronic)2184-4984

Conference

Conference9th International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE 2023
Country/TerritoryCzech Republic
CityPrague
Period22/04/2324/04/23

Keywords

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

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