Explainable-AI (XAI) dengan GradCAM terhadap Arsitektur ResNet50V2 pada Klasifikasi Luka Kaki Diabetes
Abstract
This study developed an image classification system for Diabetic Foot Ulcers (DFU) to distinguish between Abnormal (Ulcer) and Normal (Healthy Skin) classes using deep learning and Explainable Artificial Intelligence (XAI). An initial dataset of 2,855 images underwent resizing to 224×224 pixels, class balancing using SMOTE, partitioning with a 70:15:15 ratio, and data augmentation. SMOTE generated a balanced dataset of 4,624 images, while augmentation expanded the training set to 16,180 images. The comparative evaluation examined ResNet50 and ResNet50V2 based on accuracy, precision, recall, and F1-score. ResNet50 achieved an accuracy of 91.21%, whereas ResNet50V2 attained 98.99% with only seven misclassifications. ResNet50V2 was identified as the superior model, achieving performance metrics of approximately 99% across all evaluation criteria. Grad-CAM was implemented to provide visual interpretability through heatmaps highlighting the regions influencing the model's predictions. For ulcer classifications, Grad-CAM accurately highlighted the wound margins with output probabilities reaching 1.0. These results demonstrate that integrating ResNet50V2 with Grad-CAM produces both high diagnostic accuracy and transparent model reasoning, offering strong potential for computer-aided DFU screening, although validation on external clinical cohorts remains necessary.
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Program Studi Informatika, Fakultas Teknik, Universitas Tanjungpura, Kota Pontianak, Provinsi Kalimantan Barat, Indonesia.
Program Studi Pendidikan Dokter, Fakultas Kedokteran, Universitas Tanjungpura, Kota Pontianak, Provinsi Kalimantan Barat, Indonesia.
Program Studi Informatika, Fakultas Teknik, Universitas Tanjungpura, Kota Pontianak, Provinsi Kalimantan Barat, Indonesia.
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