## 3 E2E Approach
### Transformers  
Transformers were originally developed for Natural Language Processing (NLP) applications [49]. Unlike RNNs and CNNs, Transformers do not use recurrent and convolutional mechanisms. Instead, Transformers use the so-called attention mechanism which allows for parallelization. In several studies [50; 51; 52], the Transformer architecture has been adopted for PD diagnosis. [PERSON] et al. [53] implement a Vocal Tab Transformer consisting of a feature embedder, Transformer encoder, and Multilayer Perceptron (MLP) head. They name their proposed method Vocal Tab Transformer, because they obtain dysphonia measures, i.e., tabular vocal features, from the subjects' voice recordings. An XgBoost model is trained to estimate the importance of these features, of which only the most important ones are given as input to the feature embedder block. However, one of the main advantages of DNN architectures such as Transformers is that they can just take in the voice recordings as raw input [14], instead of performing a feature extraction and feature selection process, first. Although [PERSON] et al. [53] are able to reach an AUC of 0.917, other studies (e.g., [50; 51]) show even better performance, without the need to implement any additional feature extraction and selection steps.  
In [50], [PERSON] et al. use Wav2 vec2.0 [54] as a backbone model and reach an accuracy of 97.92% for the classification of PD and healthy control subjects. The authors also attempt to predict the severity of the disease based on the Hoehn and Yahr (H&Y) scale. This attempt is less successful. Unfortunately, the authors do not mention the actual performance for this task, making it difficult to determine the extent of their lack of success.  
[PERSON] et al. [51] reach an accuracy of 98.5% using a Swin Transformer for the classification of PWP and HC. The margin with the CNN architecture they implemented, a VGG16, is not that large, however. With the VGG16, an accuracy of 98.1% was achieved, which is only 0.4 percentage point lower than the accuracy that was reached with the Swin Transformer. Nevertheless, it shows the potential of Transformer architectures for PD classification. Furthermore, the authors discriminate between HC, mild PD, and severe PD, based on the Unified Parkinson's Disease Rating Scale (UPDRS). They show that the Swin Transformer has strong capabilities to discriminate between HC and PD, showing a precision of 95%. However, discriminating between only two classes, mild and severe PD, appears to be more difficult, resulting in a precision of 85%. Although the recognition of different stages of PD is still not optimal, there already seems to be some improvement compared to the unsuccessful attempt to discriminate between five different classes in [50].  
In Section 3, it was mentioned that [26] use several CNN and Transformer models to classify PD. For each of these models, the authors use also the TL approach, which will be discussed in more detail in Section 4. The Transformer models are the ViT-L-32 and ViT-B-16, which are both Vision Transformers (ViT), and lead to an accuracy of 96.74% and 91.57%, respectively. For most of the CNN models, such as ResNet50 (see Section 3 for more details), a higher performance was reported than for these Transformers, although the margin is small. The authors conclude from this that the latest and more advanced models are not necessarily better in terms of performance than the more conventional ones.