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The work presented in this article used the EfficientNetV2B0 model for categorization of diabetic foot ulcers (DFU) using a Kaggle DFU data set data set, which contains a total of 844 images consisting of normal and ulcerated images, each represented 50%. The model achieved a high test accuracy of 98.7%, with precision, recall and F1-score equal to 98.8%, 97.9%, and 98.3%, respectively. The conflict analysis in the latter section reveals the advantage of using EfficientNetV2B0 as it manages to outperform ResNet-50, VGG16, and MobileNetV2 not only in performance, but also in cost efficiency efforts. This article highlights the potential applications of EfficientNetV2B0 proposed in the areas of telemedicine and remote medical services, thus solving very important issues of early detection and classification of diabetic foot ulcers.

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EfficientNetV2B0 for Automated Diabetic Foot Ulcer Detection: Implications for Telemedicine and Clinical Practice

  • 会议论文

  • 发表日期：

    2026年

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  • C. H. Rajani,
  • Putluri Jagadeeswara Reddy,
  • Gangavarapu Jashuva,
  • Jammigumpula Lakshminarayana,
  • S. V. N. Srinivasu,
  • Sireesha Moturi

摘要

The work presented in this article used the EfficientNetV2B0 model for categorization of diabetic foot ulcers (DFU) using a Kaggle DFU data set data set, which contains a total of 844 images consisting of normal and ulcerated images, each represented 50%. The model achieved a high test accuracy of 98.7%, with precision, recall and F1-score equal to 98.8%, 97.9%, and 98.3%, respectively. The conflict analysis in the latter section reveals the advantage of using EfficientNetV2B0 as it manages to outperform ResNet-50, VGG16, and MobileNetV2 not only in performance, but also in cost efficiency efforts. This article highlights the potential applications of EfficientNetV2B0 proposed in the areas of telemedicine and remote medical services, thus solving very important issues of early detection and classification of diabetic foot ulcers.