Abstract
Despite the existence of patterns able to discriminate between consensual and non-consensual intercourse, the relevance of genital lesions in the corroboration of a legal rape complaint is currently under debate in many countries. The testimony of the physicians when assessing these lesions has been questioned in court due to several factors (e.g. a lack of comprehensive knowledge of lesions, wide spectrum of background area, among others). Thereby, it is relevant to provide automated tools to support the decision process in an objective manner. In this work, we compare traditional handcrafted features and deep learning techniques in the automated processing of colposcopic images for genital injury detection. Positive results where achieved by both paradigms in segmentation and classification subtasks, being traditional and deep models the best strategy for each subtask type respectively.
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Acknowledgements
This work was funded by the Project “NanoSTIMA: Macro-to-Nano Human Sensing: Towards Integrated Multimodal Health Monitoring and Analytics/NORTE-01-0145-FEDER-000016” financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF), and also by Fundação para a Ciência e a Tecnologia (FCT) within PhD grant number SFRH/BD/93012/2013.
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Fernandes, K., Cardoso, J.S., Astrup, B.S. (2017). Automated Detection and Categorization of Genital Injuries Using Digital Colposcopy. In: Alexandre, L., Salvador Sánchez, J., Rodrigues, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2017. Lecture Notes in Computer Science(), vol 10255. Springer, Cham. https://doi.org/10.1007/978-3-319-58838-4_28
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DOI: https://doi.org/10.1007/978-3-319-58838-4_28
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