Journal Article2026

FootSegONN: an ensemble of Self-ONN-based models for diabetic foot ulcer segmentation

Md Shaheenur Islam Sumon, Saadia Binte Alam, Rashedur Rahman, Rusab Sermun, Md Mezbah Ahmed Mahedi, Zaid Bin Mahbub, Rumana Habib, Muhammad EH Chowdhury

Neural Computing and Applications

Springer, Vol. 38, Issue 5, pp. 121, ISBN: 0941-0643

  • Good Health and Well-being — Relevance: 95%
DOI: 10.1007/s00521-025-11747-9URL

Abstract

Abstract Diabetes mellitus is a chronic metabolic disorder characterized by persistent high blood sugar levels due to insulin deficiencies, leading to complications such as diabetic foot ulcers (DFUs). DFUs are associated with high morbidity and a significant risk of amputation, necessitating precise monitoring and management. Manual measurement of ulcer areas is labor-intensive and error-prone, prompting the need for automated, computer-aided methods. Deep learning (DL) techniques have shown promise in this domain, enhancing the accuracy and efficiency of ulcer detection and segmentation. This study investigates various methodologies for DFU segmentation, focusing on advanced DL models. We propose FootSegONN, an EfficientNet-based encoder and Self-organized Operational Neural Network (Self-ONN) and Feature Pyramid Network-based decoder for foot ulcer segmentation, evaluated on a publicly available chronic wound dataset consisting of 1010 diabetic foot images. Self-ONNs address the limitations of conventional Convolutional neural networks by achieving ultimate heterogeneity and boosting network diversity while maintaining computational efficiency. To ensure robust validation, fivefold cross-validation was applied, and more than 10 different segmentation models were utilized. The STAPLE algorithm is employed to combine mask predictions from top-performing models, and a post-processing approach is investigated to enhance performance further. The proposed method achieved a state-of-the-art Dice score of 91.55%. Furthermore, the combined FootSegONN model achieved a Dice score of 88.97% on the external validation dataset. Gradient-weighted class activation map (Grad-CAM) visualization was utilized to assess the model’s interpretability.

Keywords

  • Diabetic foot ulcer
  • Grad-CAM interpretability

CCDS Authors

Saadia Binte Alam, PhD
Saadia Binte Alam, PhD

Saadia Binte Alam, PhD

  • Director, MIRA & WiSE Wings

Associate Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Published at

HCII, Scientific Reports, Cognitive Computation, Neural Computing and Applications

Md. Rashedur Rahman, D. Eng.
Md. Rashedur Rahman, D. Eng.

Md. Rashedur Rahman, D. Eng.

  • Co-Director, MIRA & NEST Wings

Assistant Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Published at

CHI, HCII, Scientific Reports, Cognitive Computation, Neural Computing and Applications

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