## 1 Introduction  
Machine learning (ML) is a subfield of artificial intelligence (AI) that involves the development of algorithms capable of learning patterns and making predictions based on data. It is a broad field that encompasses different approaches and techniques, including deep learning (DL). Deep learning is a subset of ML that involves the use of artificial neural networks (ANNs) with multiple layers to learn patterns from data [1]. The neural networks in deep learning can have many layers, making it possible to extract complex features from data.  
One popular application of DL is computer vision, where convolutional neural networks (CNNs) have proven to be very effective in image recognition tasks. CNNs use convolutional layers to extract features from images and pool those features to reduce the dimensionality of the data, allowing them to identify patterns and classify images into different categories [2].  
Pre-trained CNN models are models that have already been trained on large datasets, such as the ImageNet dataset, making them useful for transfer learning on other datasets. Examples of such models are the winners of the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC), including DenseNets, ResNets, and VGGs [3].  
However, there has recently been growing interest in a new type of DL architecture called vision transformers (ViTs) [4], which are based on the transformer architecture commonly used in natural language processing (NLP) tasks. The ViT encoder uses a self-attention mechanism to extract features from the input image, allowing the model to consider the entire image at one time and identify important regions. This makes ViTs more efficient in identifying global features in images, such as overall shape and color, and permits them to learn more complex relationships between different parts of the image. Also, ViTs use residual links to forward extracted features to an MLP head unaffected by its depth.  
This work aimed to investigate the performance and optimization learning of two deep neural networks, ImageNet CNNs and the Google ViT, in classifying four white blood cell (WBC) types (neutrophil, eosinophil, lymphocyte, and monocyte) by means of transfer learning. This study used the PBC [5] and BCCD [6] datasets. PBC is a large imbalanced dataset with high-quality images, while BCCD is a small imbalanced dataset with poor-quality images. Data augmentation techniques were employed to increase the size of the BCCD dataset.  
The paper will proceed with a literature review of the relevant research related to the detection and classification of WBCs using pre-trained CNNs and ViTs in Section 2. Section 3 will describe the methodology used in this study, while Section 4 will present the experimental results obtained using pre-trained ILSVRC models and the Google ViT for blood cell classification. An in-depth analysis of the results will be provided in Section 5, and the paper will be concluded in Section 6.  
Overall, the paper explores the effectiveness of pre-trained deep learning models in classifying WBC types from peripheral blood smear images. Transfer learning and data augmentation techniques were employed to address the imbalanced and poor-quality nature of the datasets. The results of the study can help improve the accuracy and efficiency of WBC classification, which could lead to the better diagnosis and treatment of blood disorders.