Abstract
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.
Keywords
- Skin wounds
- Mobile health
- Object detection
- Deep learning
- Mobile devices
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Acknowledgments
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.
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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. https://doi.org/10.1007/978-3-030-39343-4_6
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DOI: https://doi.org/10.1007/978-3-030-39343-4_6
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