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Diabetic foot thermal image segmentation using Double Encoder-ResUnet (DE-ResUnet).

Doha Bouallal1, Hassan Douzi1, Rachid Harba2

  • 1IRF-SIC Laboratory, Ibn Zohr University, Agadir, Morocco.

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Summary

This study introduces a deep learning model for precise diabetic foot segmentation using thermal and RGB images. The DE-ResUnet achieves 97% IoU, improving early detection of ulceration risk areas.

Keywords:
Diabetic footdeep learningimage segmentationmobile healththermal imaging

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Diabetology

Background:

  • Diabetic Foot (DF) ulceration is a significant complication of diabetes.
  • Early detection of DF requires precise segmentation of foot thermal images.
  • Current segmentation methods lack accuracy and reliability for clinical use.

Purpose of the Study:

  • To develop a fully automated, robust, and accurate segmentation method for the diabetic foot.
  • To improve the identification of high-risk areas for ulceration in diabetic patients.

Main Methods:

  • A novel deep neural network, Double Encoder-ResUnet (DE-ResUnet), was developed.
  • The model fuses thermal and RGB (Red, Green, Blue) image data.
  • A dataset of 398 image pairs from healthy and diabetic subjects was utilized.

Main Results:

  • The DE-ResUnet model achieved an average Intersection over Union (IoU) of 97%.
  • The model demonstrated superior performance compared to state-of-the-art methods.
  • Accurate delineation of high-risk regions like toes and heels was achieved.

Conclusions:

  • The proposed DE-ResUnet offers a robust and accurate solution for automated diabetic foot segmentation.
  • This technology has the potential to enhance early diagnosis and prevention of diabetic foot ulcers.