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

TL;DR

This study compares pre-trained CNNs, Transformer models, and DINOv2 for diagnosing Cushing's syndrome from facial images, finding that Transformer-based models and DINOv2 outperform CNNs, especially in capturing global facial features.

Contribution

It provides a comprehensive comparison of CNNs, Transformer models, and DINOv2 for Cushing's syndrome diagnosis, highlighting the superior performance of Transformer-based models and DINOv2.

Findings

Transformer models outperform CNNs in accuracy.

DINOv2 achieves the highest F1 score of 85.74%.

Gender bias favors female samples in model accuracy.

Abstract

Cushing's syndrome is a condition caused by excessive glucocorticoid secretion from the adrenal cortex, often manifesting with moon facies and plethora, making facial data crucial for diagnosis. Previous studies have used pre-trained convolutional neural networks (CNNs) for diagnosing Cushing's syndrome using frontal facial images. However, CNNs are better at capturing local features, while Cushing's syndrome often presents with global facial features. Transformer-based models like ViT and SWIN, which utilize self-attention mechanisms, can better capture long-range dependencies and global features. Recently, DINOv2, a foundation model based on visual Transformers, has gained interest. This study compares the performance of various pre-trained models, including CNNs, Transformer-based models, and DINOv2, in diagnosing Cushing's syndrome. We also analyze gender bias and the impact of freezing mechanisms on DINOv2. Our results show that Transformer-based models and DINOv2 outperformed CNNs, with ViT achieving the highest F1 score of 85.74%. Both the pre-trained model and DINOv2 had higher accuracy for female samples. DINOv2 also showed improved performance when freezing parameters. In conclusion, Transformer-based models and DINOv2 are effective for Cushing's syndrome classification.