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Related Experiment Video

Updated: Jul 16, 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

ODQN-Net: Optimized Deep Q Neural Networks for Disease Prediction Through Tongue Image Analysis Using Remora

S V N Sreenivasu¹, P Santosh Kumar Patra², Vasujadevi Midasala³

¹Department of Computer Science and Engineering, Narasaraopeta Engineering College (A), Narasaraopet, India.

|September 13, 2023

Summary

This summary is machine-generated.

This study introduces an optimized deep Q-neural network (ODQN-Net) for accurate disease prediction from tongue images, improving upon traditional methods. The ODQN-Net achieves high accuracy in classifying multiple diseases using enhanced image processing and feature extraction techniques.

Keywords:

Indian ayurvedic medicine Remora optimization algorithm deep q-neural network local ternary pattern tongue image analysis

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Area of Science:

Artificial Intelligence
Medical Imaging
Traditional Indian Medicine

Background:

Traditional Ayurvedic medicine relies on manual tongue analysis for disease diagnosis, which is time-consuming and lacks precision.
Existing machine learning models for tongue-based disease prediction have not achieved sufficient accuracy, especially for multiclass classification.
Accurate and automated disease identification from tongue images remains a significant challenge.

Purpose of the Study:

To develop an optimized deep Q-neural network (ODQN-Net) for enhanced disease identification and classification from tongue images.
To improve the accuracy and efficiency of disease prediction compared to existing methods.
To address the limitations of manual diagnosis and current AI approaches in Ayurvedic medicine.

Main Methods:

Image enhancement using the multiscale retinex approach for quality improvement and noise reduction.
Feature extraction utilizing the local ternary pattern for color-based analysis and the Remora optimization algorithm for efficient selection.
Classification of diseases using an optimized deep Q-neural network (ODQN-Net) model.

Main Results:

The proposed ODQN-Net achieved a high accuracy of 99.17% on a tongue imaging dataset.
Excellent performance metrics were recorded, including an F1-score of 99.75% and a Mathew's correlation coefficient of 99.84%.
The ODQN-Net demonstrated superior performance compared to current state-of-the-art approaches.

Conclusions:

The ODQN-Net model offers a highly accurate and efficient solution for automated disease prediction and classification from tongue images.
This AI-driven approach has the potential to revolutionize Ayurvedic diagnostics by overcoming the limitations of manual inspection.
The study highlights the effectiveness of combining advanced deep learning with optimized feature extraction for medical image analysis.