CrossViT with ECAP: Enhanced deep learning for jaw lesion classification.

BACKGROUND

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.

METHODS

We conducted a retrospective study involving 208 prevalent radiolucent jaw lesions (49 AMs, 59 DCs, 48 OKCs, and 54 RCs) observed in panoramic radiographs or orthopantomograms (OPGs) with confirmed histological diagnoses. Three experienced oral radiologists provided annotations with consensus. We implemented horizontal flip and ECAP technique with CrossViT-15, -18, ResNet-50, -101, and -152. A four-fold cross-validation approach was employed. The models' performance assessed through accuracy, specificity, precision, recall (sensitivity), F1-score, and area under the receiver operating characteristics (AUCs) metrics.

RESULTS

Models using the ECAP technique generally achieved better results, with ResNet-152 showing a statistically significant increase in F1-score. CrossViT models consistently achieved higher accuracy, precision, recall, and F1-score compared to ResNet models, regardless of ECAP usage. CrossViT-18 achieved the best overall performance. While all models showed positive ability to differentiate lesions, DC had the highest AUCs (0.89-0.90) and OKC the lowest (0.72-0.81). Only CrossViT-15 achieved AUCs above 0.80 for all four lesion types.

CONCLUSION

ECAP, a targeted padding data technique, improves deep learning model performance for radiolucent jaw lesion classification. This context-preserving approach is beneficial for tasks requiring an understanding of the lesion's surroundings. Combined with CrossViT models, ECAP shows promise for accurate classification, particularly for rare lesions with limited data.