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FCFNet: A Network Fusing Color Features and Focal Loss for Diabetic Foot Ulcer Image Classification

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Neural Information Processing (ICONIP 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1793))

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Abstract

Diabetic foot ulcers (DFU) are one of the most common and severe complications of diabetes. More than one million diabetics face amputation due to failure to recognize and treat DFU properly every year, and it is very important to early recognize the condition of DFU. As deep learning has achieved outstanding results in computer vision tasks, some deep learning methods have been used for diabetic foot ulcer image classification. However, these models do not pay attention to the learning of color information in images and may not effectively learn the characteristics of hard examples, which are difficult to recognize by models trained by common loss functions. In addition, they may be time-consuming or not suitable for small datasets. According to DFU medical classification systems, the presence of infection and ischemia has important clinical implications for DFU assessment. In our work, based on the characteristics of gangrene and wound, which are significantly different from the surrounding skin and the background, we use the K-Means algorithm to segment the features and further build the classification model with the segmentation module. Next, we use focal loss to train EfficientNet B3, which can make this model better learn the characteristics of hard examples. We finally use the two powerful networks for testing. The experimental results demonstrate that compared with other excellent classification models, our model has better performance with a macro-average F1-score of 0.6334, which has great potential in medical assisted diagnosis.

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Acknowledgements

We have obtained the authorization of the organizers of DFUC 2021 for research, who have received approval from the UK National Health Service (NHS) Research Ethics Committee (REC). The computation is supported by the School of Computer Engineering and Science of Shanghai University. This work is supported by the NSFCs of China (No. 61902234).

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Authors and Affiliations

  1. School of Computer Engineering and Science, Shanghai University, Shanghai, China

    Chuantao Xie

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  1. Chuantao Xie
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Correspondence to Chuantao Xie .

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Editors and Affiliations

  1. Indian Institute of Technology Indore, Indore, India

    Mohammad Tanveer

  2. Indian Institute of Information Technology - Allahabad, Prayagraj, India

    Sonali Agarwal

  3. Kobe University, Kobe, Japan

    Seiichi Ozawa

  4. Indian Institute of Technology Patna, Patna, India

    Asif Ekbal

  5. University of Innsbruck, Innsbruck, Austria

    Adam Jatowt

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Xie, C. (2023). FCFNet: A Network Fusing Color Features and Focal Loss for Diabetic Foot Ulcer Image Classification. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Communications in Computer and Information Science, vol 1793. Springer, Singapore. https://doi.org/10.1007/978-981-99-1645-0_36

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  • DOI: https://doi.org/10.1007/978-981-99-1645-0_36

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  • Print ISBN: 978-981-99-1644-3

  • Online ISBN: 978-981-99-1645-0

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