DFU-GCNet: a global context-enhanced inception network for robust and interpretable diabetic foot ulcer classification - Summary - MDSpire

DFU-GCNet: a global context-enhanced inception network for robust and interpretable diabetic foot ulcer classification

  • By

  • Md. Tofael Ahmed Bhuiyan

  • Md. Abdur Rahman

  • Farzan Majeed Noori

  • Md Zia Uddin

  • Abdul Kadar Muhammad Masum

  • May 28, 2026

  • 0 min

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Objective:

To introduce DFU-GCNet for robust and interpretable classification of diabetic foot ulcers (DFUs).

Key Findings:
  • DFU-GCNet achieved a classification accuracy of 97.16%.
  • The model recorded an F1-score of 0.9715 and a Matthews correlation coefficient of 0.9437.
  • DFU-GCNet outperformed standardized modern baselines like VGG16 and EfficientNet.
Interpretation:

DFU-GCNet is a highly reliable automated screening tool for diabetic foot ulcers.

Limitations:
Conclusion:

The study highlights the importance of automated diagnostic systems in improving clinical management of DFUs.

Original Source(s)

DFU-GCNet: a global context-enhanced inception network for robust and interpretable diabetic foot ulcer classification

  • by Md. Tofael Ahmed Bhuiyan, Md. Abdur Rahman, Farzan Majeed Noori, Md Zia Uddin, Abdul Kadar Muhammad Masum

  • May 28, 2026

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