Wireless capsule endoscopy (WCE) is a noninvasive medical imaging method that provides real-time visual data on the condition of the digestive system. This technique allows for the identification and diagnosis of diverse gastrointestinal disorders. Offering a more patient-friendlier alternative to conventional endoscopic procedures, WCE ensures minimal discomfort and invasiveness while offering valuable insights into the health of the digestive system. The proposed work represents a comprehensive investigation into ulcer classification using WCE images. Leveraging the transformative capabilities of Vision Transformer (ViT), the study employs advanced image processing techniques, namely Image Rescaling and Color Space Transformation, to enhance the dataset’s image quality. The ViT is then utilized for effective ulcer image classification. Furthermore, this chapter conducts an ablation study involving four prominent deep learning models—ResNet 50, VGG19, Inception V3, and InceptionResNet152V2—to benchmark their performance against ViT. The comparative analysis reveals that ViT outperforms traditional CNN models, setting a significant benchmark for its efficacy in medical diagnosis. The findings underscore the potential of Vision Transformer in enhancing the accuracy and reliability of ulcer classification in medical imaging.