Comparative Analysis of Pre-trained Deep Learning Models and DINOv2 for Cushing’s Syndrome Diagnosis in Facial Analysis

Abstract

Cushing’s syndrome is caused by excessive glucocor-ticoid secretion and often presents with facial features such as moon facies and plethora, making facial images valuable for diagnosis. Recent studies have used pre-trained CNNs for automated diagnosis using frontal facial images. However, CNNs focus on local features and may miss global facial characteristics typical of Cushing’s syndrome. Transformer-based visual models, through self-attention mechanisms, are better suited for capturing global features. The foundational model DINOv2, also based on the vision Transformer architecture, has recently attracted attention. In this study, we compared the performance of various pre-trained models, including CNNs, Transformer-based models, and foundational models like DINOv2, in a transfer learning setting. We also investigated biological sex bias and the effect of freezing mechanisms on DINOv2. Our results show that Transformer-based models, especially ViT, and DINOv2 outperformed CNNs, with ViT achieving the highest F1 score of 85.74%. All models showed higher accuracy for female samples, likely due to biological sex imbalance. Freezing mechanisms notably improved DINOv2 performance. In conclusion, Transformer-based models and DINOv2 show strong potential for classifying Cushing’s syndrome using facial images.The source code is publicly available at: https://github.com/songchangwei/Cushing-Disease-Diagnosis.

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