CrossViT with ECAP: Enhanced deep learning for jaw lesion classification

Radiolucent jaw lesions like ameloblastoma (AM), dentigerous cyst (DC), odontogenic keratocyst (OKC), and radicular cyst (RC) often share similar characteristics, making diagnosis challenging. In 2021, CrossViT, a novel deep learning approach using multi-scale vision transformers (ViT) with cross-attention, emerged for accurate image classification. Additionally, we introduced Extended Cropping and Padding (ECAP), a method to expand training data by iteratively cropping smaller images while preserving context. However, its application in dental radiographic classification remains unexplored. This study investigates the effectiveness of CrossViTs and ECAP against ResNets for classifying common radiolucent jaw lesions.