Tenze, L.; Canessa, E. NAILS: Normalized Artificial Intelligence Labeling Sensor for Self-care Health. Preprints2024, 2024102375. https://doi.org/10.20944/preprints202410.2375.v1
APA Style
Tenze, L., & Canessa, E. (2024). NAILS: Normalized Artificial Intelligence Labeling Sensor for Self-care Health. Preprints. https://doi.org/10.20944/preprints202410.2375.v1
Chicago/Turabian Style
Tenze, L. and Enrique Canessa. 2024 "NAILS: Normalized Artificial Intelligence Labeling Sensor for Self-care Health" Preprints. https://doi.org/10.20944/preprints202410.2375.v1
Abstract
Visual examination of nails can reflect the human health status. Diseases such as nutritive imbalances and skin diseases can be identified by looking the colors around the plate part of the nails. We present the AI-based NAILS method to detect fingernails through segmentation and labeling. We built a generalized model to identify single fingers, their fingernail plates and retrieve their color data. A pre-trained deep learning Neural Network algorithm containing different images of fingernails as reference frames is used to identify different fingernails edges and the color areas around them in real-time. The novel aspect of our NAILS sensor model is that it allows to analyze –via a GUI and the use of a HD webcam, the tiniest dynamical signals from changes in fingernail colors. This feature could be used to self-extract primary signs of diseases in humans.
Keywords
region-based image segmentation; machine learning algorithm; fingernails color analysis; early disease detection
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
