Automatic detection of fungiform papillae on the human tongue via Convolutional Neural Networks and identification of the best performing model
Our results show that the Optimized U-Net achieved the highest stability, accuracy, and robustness in learning and prediction of FPs with challenging morphologies. The ability to automatically detect FPs has important implications for understanding individual differences in taste perception, which could eventually help in diagnosing taste disorders or guiding personalized nutrition plans.
Authors
- Raffaella Fiamma Cabini (ORCID: https://orcid.org/0000-0003-2983-989X)
- Roberto Crnjar (ORCID: https://orcid.org/0000-0003-4685-1348)
- Iole Tomassini Barbarossa (ORCID: https://orcid.org/0000-0002-2849-4052)
- Diego Ulisse Pizzagalli (ORCID: https://orcid.org/0000-0001-7158-5323)
- Melania Melis (ORCID: https://orcid.org/0000-0003-3410-3158)
- Lala Chaimae Naciri (ORCID: https://orcid.org/0000-0002-1721-6815)
Institutions
- University of Cagliari (IT)
- Istituto Universitario di Studi Superiori di Pavia (IT)
- Università della Svizzera italiana (CH)
Publication Details
- Journal
- Computational and Structural Biotechnology Journal
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1016/j.csbj.2025.05.014
- Primary Topic
- Biochemical Analysis and Sensing Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Fondazione di Sardegna
- Ministero della Salute
- Universidad de Ciego de Ávila