Biswas Shuvo, Mostafiz Rafid, Uddin Mohammad Shorif, Paul Bikash Kumar
Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Bangladesh.
Institute of Information Technology, Noakhali Science and Technology University, Bangladesh.
Heliyon. 2024 May 14;10(10):e31228. doi: 10.1016/j.heliyon.2024.e31228. eCollection 2024 May 30.
Diabetic foot ulcer (DFU) poses a significant threat to individuals affected by diabetes, often leading to limb amputation. Early detection of DFU can greatly improve the chances of survival for diabetic patients. This work introduces FusionNet, a novel multi-scale feature fusion network designed to accurately differentiate DFU skin from healthy skin using multiple pre-trained convolutional neural network (CNN) algorithms. A dataset comprising 6963 skin images (3574 healthy and 3389 ulcer) from various patients was divided into training (6080 images), validation (672 images), and testing (211 images) sets. Initially, three image preprocessing techniques - Gaussian filter, median filter, and motion blur estimation - were applied to eliminate irrelevant, noisy, and blurry data. Subsequently, three pre-trained CNN algorithms -DenseNet201, VGG19, and NASNetMobile - were utilized to extract high-frequency features from the input images. These features were then inputted into a meta-tuner module to predict DFU by selecting the most discriminative features. Statistical tests, including Friedman and analysis of variance (ANOVA), were employed to identify significant differences between FusionNet and other sub-networks. Finally, three eXplainable Artificial Intelligence (XAI) algorithms - SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), and Grad-CAM (Gradient-weighted Class Activation Mapping) - were integrated into FusionNet to enhance transparency and explainability. The FusionNet classifier achieved exceptional classification results with 99.05 % accuracy, 98.18 % recall, 100.00 % precision, 99.09 % AUC, and 99.08 % F1 score. We believe that our proposed FusionNet will be a valuable tool in the medical field to distinguish DFU from healthy skin.
糖尿病足溃疡（DFU）对糖尿病患者构成了重大威胁，常常导致肢体截肢。早期检测DFU能够极大地提高糖尿病患者的存活几率。这项工作引入了FusionNet，这是一种新颖的多尺度特征融合网络，旨在使用多种预训练卷积神经网络（CNN）算法准确区分DFU皮肤和健康皮肤。一个包含来自不同患者的6963张皮肤图像（3574张健康图像和3389张溃疡图像）的数据集被分为训练集（6080张图像）、验证集（672张图像）和测试集（211张图像）。最初，应用了三种图像预处理技术——高斯滤波器、中值滤波器和运动模糊估计——来消除无关、有噪声和模糊的数据。随后，利用三种预训练的CNN算法——DenseNet201、VGG19和NASNetMobile——从输入图像中提取高频特征。然后将这些特征输入到一个元调优模块中，通过选择最具判别力的特征来预测DFU。采用包括弗里德曼检验和方差分析（ANOVA）在内的统计测试来确定FusionNet与其他子网络之间的显著差异。最后，将三种可解释人工智能（XAI）算法——SHAP（Shapley值加法解释）、LIME（局部可解释模型无关解释）和Grad-CAM（梯度加权类激活映射）——集成到FusionNet中，以提高透明度和可解释性。FusionNet分类器取得了优异的分类结果，准确率为99.05%，召回率为98.18%，精确率为100.00%，AUC为99.09%，F1分数为99.08%。我们相信，我们提出的FusionNet将成为医学领域中区分DFU和健康皮肤的宝贵工具。
