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Related Experiment Video

Updated: May 24, 2025

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Tongue shape classification based on IF-RCNet.

Tiantian Liang¹, Haowei Wang¹, Wei Yao²

¹School of Electrical Engineering, Dalian Jiaotong University, 794 Huanghe Road, Dalian, 116028, China.

Scientific Reports

|March 2, 2025

Summary

This summary is machine-generated.

This study introduces IF-RCNet, a novel network for accurate tongue shape classification, overcoming individual variations and lip interference. It improves diagnostic objectivity, especially with limited data.

Keywords:

Deep learning Feature fusion Mixed input Tongue diagnosis

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Last Updated: May 24, 2025

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Ultrasound Images of the Tongue: A Tutorial for Assessment and Remediation of Speech Sound Errors

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Published on: January 3, 2017

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Objective tongue shape classification is crucial for medical diagnoses.
Existing methods face challenges due to inter-individual variations, lip interference, and small datasets.

Purpose of the Study:

To develop an accurate and robust tongue shape classification network.
To address limitations of current methods in objective tongue diagnosis.

Main Methods:

A two-level nested network, IF-RCNet, was developed, integrating tongue segmentation (RCA-UNet) and classification (RCA-Net).
Feature fusion and mixed input strategies were employed to enhance feature extraction and network input.

Main Results:

IF-RCNet demonstrated superior performance compared to VGG16, ResNet18, AlexNet, ViT, and MobileNetv4.
The network accurately classified tongue shapes despite individual differences and lip interference, performing well on small datasets.

Conclusions:

IF-RCNet offers a new, effective approach for tongue shape classification, enhancing diagnostic accuracy.
The method shows promise for objective tongue diagnosis, particularly in resource-limited scenarios.