Diabetic Foot Ulcers (DFU) require timely severity assessment to guide treatment and follow-up. This paper investigates automated four-class DFU severity classification (Wagner-Meggitt Grades 1-4) using transfer learning on a public Kaggle dataset. The dataset includes 10062 images with roughly 446-450 subjects per grade. To ensure leakage-free evaluation, we extract subject IDs from standardized filenames and employ subject-wise 5-fold cross-validation. We compare five ImageNet-pretrained CNNs: ResNet18, ResNet50, DenseNet121, GoogLeNet, and MobileNetV2. We further study the effect of dataset reliability by evaluating Kaggle as provided and ( an expert-verified, quality-screened (QC) subset obtained by removing mislabeled and low-quality samples (1539 images). Augmentation is applied only during training with two strengths (4 vs. 10 augmented copies per image), while validation and test sets remain unchanged. DenseNet121 attains 62.78% accuracy on the Kaggle setting, whereas QC with stronger augmentation achieves 89.23% accuracy, indicating that expert verification and controlled augmentation substantially improve robustness for DFU severity classification.