Mahmud Monirul Islam, Reza Md Shihab, Akash Mohammad Olid Ali, Elias Farhana, Ahmed Nova
Department of Electrical and Computer Engineering, North South University, Dhaka, Bangladesh.
Design Inclusion and Access Lab, North South University, Dhaka, Bangladesh.
PLoS One. 2025 Sep 2;20(9):e0330669. doi: 10.1371/journal.pone.0330669. eCollection 2025.
Diabetic Foot Ulcer (DFU) is a major complication of diabetes which needs early detection to help in timely treatment for preventing future serious consequences. Due to peripheral neuropathy, high blood glucose levels, and untreated wounds, DFUs can cause the disintegration of the skin and exposing the tissue below it, if not adequately treated. Recently deep learning (DL) has advanced and has shown its ability to automate DFU detection and classification by analysing medical images. The use of DL has been proven to be very useful for healthcare professionals, enabling earlier diagnosis and effective treatment of DFU. However, most of the studies predominantly rely on a single dataset (e.g., DFUC2021 or DFUC2020) without external validation or cross-dataset testing, raising concerns about generalizability and trustworthiness. The aim of this study is to develop a robust, reliable, and transparent DFU detection framework which is not only good performing but also can effectively give attention to the proper region of the images which are crucial for DFU detection. So, to make DFU detection robust, reliable in a single study, we proposed a custom approach, DFU_DIALNet and to enhance transparency and interpret the model decisions in this study, we integrated Grad-CAM and LIME heatmaps to precisely localize ulcer regions. This allows visual verification of the model's focus and clarifies the decision-making process, thereby increasing the model's reliability. DFU_DIALNet outperforms all other traditional models with 99.33% accuracy, 99% F1 score, and 100% AUC score, and compared it to other DL models-DenseNet121, MobileNetV2, InceptionV3, EfficientNetB0, ResNet50V2 and VGG16-in the merged dataset of DFUC2021 with our collected 500 images. We have checked our model's reliability with 2 other popular datasets--the KDFU and DFUC2020 datasets, where our proposed approach gives the highest accuracy of 95.61% and 99.54%, respectively, compared to other deep learning approaches. Lastly, we have developed a web app using Streamlit to detect DFU efficiently. This study fills the gap between reliable and interpretable systems with a proposed approach to the efficient detection of DFU.
糖尿病足溃疡（DFU）是糖尿病的一种主要并发症，需要早期检测以帮助及时治疗，预防未来的严重后果。由于周围神经病变、高血糖水平和伤口未得到治疗，如果DFU没有得到充分治疗，可能会导致皮肤溃烂，使下方组织暴露。近年来，深度学习（DL）取得了进展，并通过分析医学图像展示了其自动进行DFU检测和分类的能力。事实证明，DL的应用对医疗保健专业人员非常有用，能够实现DFU的早期诊断和有效治疗。然而，大多数研究主要依赖单一数据集（如DFUC2021或DFUC2020），没有进行外部验证或跨数据集测试，这引发了对模型通用性和可信度的担忧。本研究的目的是开发一个强大、可靠且透明的DFU检测框架，该框架不仅性能良好，而且能够有效地关注对DFU检测至关重要的图像特定区域。因此，为了在单一研究中使DFU检测稳健、可靠，我们提出了一种定制方法DFU_DIALNet，并且为了提高透明度并解释本研究中的模型决策，我们整合了Grad-CAM和LIME热图以精确地定位溃疡区域。这使得能够直观地验证模型的关注点，并阐明决策过程，从而提高模型的可靠性。在DFUC2021与我们收集的500张图像的合并数据集中，DFU_DIALNet的准确率为99.33%，F1分数为99%，AUC分数为100%，优于所有其他传统模型，并与其他深度学习模型——DenseNet121、MobileNetV2、InceptionV3、EfficientNetB0、ResNet50V2和VGG16进行了比较。我们使用另外两个流行数据集——KDFU和DFUC2020数据集检验了我们模型的可靠性，与其他深度学习方法相比，我们提出的方法在这两个数据集中分别给出了95.61%和99.54%的最高准确率。最后，我们使用Streamlit开发了一个网络应用程序以高效检测DFU。本研究通过提出一种高效检测DFU的方法，填补了可靠且可解释系统之间的空白。
