Wound classification is a critical task in medical diagnostics, essential for determining appropriate treatment strategies. Despite advancements, current methods often suffer from low accuracy, particularly in multi-label wound classification. This study aims to enhance the accuracy and robustness of wound image classification using the AZH Wound Image Dataset, which includes 730 Region of Interest (ROI) images and 538 whole wound images of venous, diabetic, pressure, and surgical wounds. Our methodology involved comprehensive pre-processing techniques such as resizing, rotation, flipping, affine transforms, color jitter, and normalization. Additionally, we implemented class-specific augmentation to address the unique challenges of each wound type. We employed a Vision Transformer (ViT) model with ViT_B_16_Weights pre-trained on ImageNet and conducted 5-fold cross-validation to ensure robust evaluation. The results demonstrated significant improvements with class-specific augmentation, achieving 88.47% average accuracy for whole image classification and 88.36% average accuracy for ROI classification, compared to 78.22% and 77.69%, respectively, without it. These findings highlight the superiority of class-specific augmentation in enhancing model performance. This study underscores the effectiveness of class-specific augmentation in wound classification. Moving forward, we aim to develop lightweight models suitable for embedding into web applications, thus enabling real-time and accessible wound assessment for broader clinical use.