# Automatic detection of fungiform papillae on the human tongue via Convolutional Neural Networks and identification of the best performing model

## TL;DR

This paper presents an automatic method using optimized neural networks to detect taste-related structures on the tongue, improving accuracy and reliability.

## Contribution

The Optimized U-Net model is introduced as the most effective CNN for detecting fungiform papillae with high accuracy and robustness.

## Key findings

- The Optimized U-Net achieved the lowest errors and highest similarity to ground truth in detecting fungiform papillae.
- It provided balanced detection of true positives, false negatives, and false positives across challenging images.
- The model demonstrated superior stability and robustness in learning and predicting papillae with varying morphologies.

## Abstract

Fungiform papillae (FPs) are fundamental for taste perception, as they contain the taste sensory cells responsible for detecting taste stimuli. Variations in the number and functionality of FPs among individuals lead to differences in taste perception, impacting the ability to identify nutrient-rich foods, health, and the joy of consuming tasty foods. Detecting FPs is a complex and time-consuming task, and there is no consensus on manual and automated methods for their identification and analysis. Objectives: This work aimed to provide an efficient, reliable, and automatic method for FP identification on the tongue, considering the physiological variations in morphology and distribution among subjects. Methods: We used three different Convolutional Neural Networks as a regression task on 175 images of the tongue, the Classic U-Net, the MultiResUNet, and the Optimized U-Net, designed to enhance the performance also when it must identify FPs in challenging input images. Results: The Optimized U-Net showed the best performance by achieving the lowest errors and the highest similarity between Ground Truths and prediction values, and the more balanced detection of True Positives, Untrue Negatives, and Untrue Positives. Conclusions: 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.