dc.description.abstract |
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 con-
volutional 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 ex-
Planations), 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. |
en_US |