Almufadi Nouf, Alhasson Haifa F
Department of Information Technology, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia.
Diagnostics (Basel). 2024 Aug 19;14(16):1807. doi: 10.3390/diagnostics14161807.
Diabetic foot ulcers (DFUs) represent a significant and serious challenge associated with diabetes. It is estimated that approximately one third of individuals with diabetes will develop DFUs at some point in their lives. This common complication can lead to serious health issues if not properly managed. The early diagnosis and treatment of DFUs are crucial to prevent severe complications, including lower limb amputation. DFUs can be categorized into two states: ischemia and infection. Accurate classification is required to avoid misdiagnosis due to the similarities between these two states. Several convolutional neural network (CNN) models have been used and pre-trained through transfer learning. These models underwent evaluation with hyperparameter tuning for the binary classification of different states of DFUs, such as ischemia and infection. This study aimed to develop an effective classification system for DFUs using CNN models and machine learning classifiers utilizing various CNN models, such as EfficientNetB0, DenseNet121, ResNet101, VGG16, InceptionV3, MobileNetV2, and InceptionResNetV2, due to their excellent performance in diverse computer vision tasks. Additionally, the head model functions as the ultimate component for making decisions in the model, utilizing data collected from preceding layers to make precise predictions or classifications. The results of the CNN models with the suggested head model have been used in different machine learning classifiers to determine which ones are most effective for enhancing the performance of each CNN model. The most optimal outcome in categorizing ischemia is a 97% accuracy rate. This was accomplished by integrating the suggested head model with the EfficientNetB0 model and inputting the outcomes into the logistic regression classifier. The EfficientNetB0 model, with the proposed modifications and by feeding the outcomes to the AdaBoost classifier, attains an accuracy of 93% in classifying infections.
糖尿病足溃疡（DFU）是与糖尿病相关的一项重大且严峻的挑战。据估计，约三分之一的糖尿病患者在其一生中的某个阶段会患上DFU。如果管理不当，这种常见并发症可能会导致严重的健康问题。DFU的早期诊断和治疗对于预防包括下肢截肢在内的严重并发症至关重要。DFU可分为两种状态：缺血和感染。由于这两种状态之间存在相似性，因此需要进行准确分类以避免误诊。已经使用了几种卷积神经网络（CNN）模型，并通过迁移学习进行了预训练。这些模型针对DFU不同状态（如缺血和感染）的二元分类进行了超参数调整评估。本研究旨在使用CNN模型和机器学习分类器开发一种有效的DFU分类系统，利用各种CNN模型，如EfficientNetB0、DenseNet121、ResNet101、VGG16、InceptionV3、MobileNetV2和InceptionResNetV2，因为它们在各种计算机视觉任务中表现出色。此外，头部模型作为模型中进行决策的最终组件，利用从前面层收集的数据进行精确的预测或分类。具有建议头部模型的CNN模型的结果已用于不同的机器学习分类器，以确定哪些分类器对于提高每个CNN模型的性能最有效。在对缺血进行分类时，最理想的结果是准确率达到97%。这是通过将建议的头部模型与EfficientNetB0模型集成，并将结果输入逻辑回归分类器来实现的。经过建议修改的EfficientNetB0模型，并将结果输入AdaBoost分类器，在对感染进行分类时达到了93%的准确率。
