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HCNNet: hybrid convolution neural network for automatic identification of ischaemia in diabetic foot ulcer wounds

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Abstract

The diabetic foot ulcer (DFU) is a significant medical complication for diabetic patients, which often leads to lower limb amputation. The manual identification of ischaemia in DFU is laborious, time-consuming, and costly. This study aims to develop an efficient deep learning (DL) method for the early identification of ischaemia in DFU. Therefore, a novel Convolutional Neural Network (CNN) architecture (HCNNet) is proposed by integrating multiple hybridised blocks from inception, residual, and dense modules along with appropriately placed Squeeze-and-Excitation (SE) block and intermediate transition layers. The proposed HCNNet is trained several times using various optimizer and learning rate settings to optimise its performance. It achieves promising Area Under the ROC Curve (AUC) scores of 0.999 for ischaemia identification. The experimental results show that the proposed HCNNet outperforms existing State-Of-The-Art (SOTA) methods.

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Data availability

The data that support the findings of this study are available from the authors but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are, however, available with permission from the Department of Computing and Mathematics, Manchester Metropolitan University England (URL: http://www2.docm.mmu.ac.uk/STAFF/M.Yap/dataset.php).

Notes

  1. http://www2.docm.mmu.ac.uk/STAFF/M.Yap/dataset.php.

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Authors and Affiliations

  1. Computer Science and Engineering, Siksha ‘O’ Anusandhan Deemed to be University, Bhubaneswar, Odisha, India

    Sujit Kumar Das

  2. Computer Science and Engineering, National Institute of Technology Agartala, Tripura, India

    Suyel Namasudra

  3. International Graduate School of AI, National Yunlin University of Science and Technology, Douliu, Taiwan

    Arun Kumar Sangaiah

  4. Electrical and Science Engineering, Lebanese American University, Byblos, Lebanon

    Arun Kumar Sangaiah

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  1. Sujit Kumar Das
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  2. Suyel Namasudra
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  3. Arun Kumar Sangaiah
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Contributions

SKD is the main author of this paper, who has conceived the idea and discussed it with all co-authors. He has also developed all the algorithms. SN has performed the simulations of this paper and write-up of this work. AKS has supervised the entire work, evaluated the performance, and proofread the paper.

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Correspondence to Suyel Namasudra or Arun Kumar Sangaiah.

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Das, S.K., Namasudra, S. & Sangaiah, A.K. HCNNet: hybrid convolution neural network for automatic identification of ischaemia in diabetic foot ulcer wounds. Multimedia Systems 30, 36 (2024). https://doi.org/10.1007/s00530-023-01241-4

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