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Advanced deep feature engineering with crayfish optimization for diabetes detection using tongue images.

Ghada Moh Samir Elhessewi1, Mukhtar Ghaleb2, Hany Mahgoub3

  • 1Department of Health Sciences, College of Health and Rehabilitation Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia.

Scientific Reports
|September 29, 2025
PubMed
Summary

A new deep learning technique accurately detects diabetes mellitus (DM) using tongue images, offering a non-invasive alternative to traditional blood tests. This method achieves 96.91% accuracy, improving early disease prediction.

Keywords:
Biomedical imagingCrayfish optimization algorithmDiabetes mellitusFeature engineeringTemporal convolutional networkTongue images

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Area of Science:

  • Biomedical imaging
  • Medical diagnostics
  • Artificial intelligence in healthcare

Background:

  • Diabetes mellitus (DM) is a severe metabolic disease requiring early, non-invasive diagnostic methods.
  • Current diagnostic approaches like fasting plasma glucose are invasive and time-consuming.
  • Tongue image analysis presents a promising non-invasive avenue for DM detection.

Purpose of the Study:

  • To develop an accurate, non-invasive method for diabetes mellitus detection using tongue imaging.
  • To introduce the Deep Feature Engineering with Crayfish Optimization for Accurate Diabetes Disease Detection via Tongue Image Analysis (DFECO-DDTIA) technique.
  • To enhance the accuracy and efficiency of diabetes diagnosis through advanced image analysis and optimization.

Main Methods:

  • Image preprocessing using upgraded weighted median filtering (Up-WMF) for noise reduction.
  • Feature extraction via squeeze-and-excitation-DenseNet (SE-DenseNet).
  • Classification using temporal convolutional network (TCN) and hyperparameter tuning with Crayfish Optimisation Algorithm (COA).

Main Results:

  • The DFECO-DDTIA technique achieved a high accuracy of 96.91% in diabetes detection from tongue images.
  • The method demonstrated superior performance compared to existing diagnostic models.
  • Image quality enhancement and optimized feature extraction contributed to improved diagnostic accuracy.

Conclusions:

  • The DFECO-DDTIA technique offers a highly accurate and non-invasive approach for diabetes mellitus diagnosis.
  • Deep learning and optimization algorithms show significant potential in biomedical imaging for disease detection.
  • This study highlights the efficacy of tongue image analysis for early diabetes prediction.