# Uncertainty Quantification for MLP-Mixer Using Bayesian Deep Learning
## 2 Methodology
### Datasets
#### 2.1.1 Acute Lymphoblastic Leukemia (ALL)  
In this study, the ALL image dataset created by [PERSON] et al. [18] was used to evaluate the proposed methods. Before discussing the technical details of the dataset, it is important to provide an overview of ALL.  
ALL is a malignant neoplasm cancer that affects the blood and bone marrow. The bone marrow is the soft tissue inside the bones and is responsible for producing blood cells. ALL is characterized by the overproduction of immature white blood cells, known as lymphoblasts, that do not function properly and can outnumber normal blood cells. This can cause anemia, infections, and other serious health issues [19]. There are two main types of leukemia: acute and chronic. Acute leukemia is a more aggressive form that requires immediate treatment and is characterized by the rapid production of underdeveloped blood cells. Chronic leukemia, on the other hand, takes longer to develop and may not require immediate treatment [20]. ALL is a type of acute leukemia that affects lymphoid cells, which are a specific type of white blood cell that helps strengthen the immune system. ALL is more common in children than in adults and is identified by the rapid production of underdeveloped lymphocytes or lymphoblasts. This disease can be life-threatening if not treated promptly [21].  
This study used a collection of images called the ALL dataset, which specifically focused on hematogenous, a type of underdeveloped white blood cell found in the bone marrow of children and adolescents. The dataset contains 3256 peripheral blood smear (PBS) images from 89 individuals suspected of having ALL, which were collected at Tehran's Taleqani Hospital in Iran [22]. The dataset was composed of 25 healthy individuals with benign conditions and 64 individuals who tested positive for a type of malignant lymphoblast. As stated by [PERSON] et al. [18], the main purpose of this dataset was to classify images into four categories: benign, early Pre-B, Pre-B, and Pro-B, as illustrated in Figure 3. These categories are referring to different stages of cancer development in B-cells. B-cells are a type of white blood cell that play a role in the immune system. Benign refers normal, healthy lymphocytes that have not yet become cancerous B-cells, while pro-B refers to lymphocytes that are in the earliest stages of development that have not yet developed some of the key features of mature lymphocytes. Early Pre-B cells are lymphocytes that are in the early stages of development and have not yet fully matured. Pre-B cells are lymphocytes that are in a slightly more advanced stage of development and are closer to becoming fully mature. Early Pre-B and Pre-B are commonly affected by ALL. The images were enlarged to 100\(\times\) using a Zeiss camera to ensure a suitable resolution for image processing. The reason for using this dataset in the current study is that it is relatively small, yet still allows a high level of accuracy to be achieved. Only a few researchers have used this dataset and CNNs to identify ALL using PBS, including [PERSON] et al. [23], [PERSON] et al. [24], and [PERSON] et al. [25]. Therefore, the proposed method in this study could provide additional insights and comparisons with previous research on this dataset.