# Experimenting with Extreme Learning Machine for Biomedical Image Classification
## 1 Introduction and Background
Deep learning adoption is associated with several drawbacks. For instance, it typically demands a substantial amount of data to outperform other techniques, making data availability a critical factor: as a matter of fact, there are several techniques that aim to increase the data, for instance, data augmentation exploiting computational fluid dynamics (CFD) simulations. CFD simulations are numerical methods used to analyze and predict the behavior of fluid flows. It is a branch of fluid mechanics that employs computational techniques to solve the governing equations of fluid flow, which are typically partial differential equations. CFD simulations are widely used in engineering, physics, and other scientific fields to study and understand complex fluid flow phenomena. CFD can be used for data augmentation in various ways; for example, in computational fluid dynamics, flow visualizations often generate colorful images representing the flow patterns, velocity fields, pressure distributions, and other related properties. These images can be used as synthetic data to augment existing image datasets. The synthetic images can introduce variations in flow patterns, turbulence, and other fluid properties that may not be readily available in real-world data. Additionally, training deep learning models can be exceedingly expensive due to the complex data models involved. The requirement for expensive GPUs and a considerable number of machines further adds to the cost and resource-intensive nature of deep learning [24]. This increases the cost to the users. We explore the possibility of considering ELM for biomedical image classification. Two biomedical domains (the first one related to the dermatoscopic images while the second one obtained with blood cell microscope) are considered for a total of four different case studies (multiclass and binary classification with dermatoscopic images and multiclass and binary classification with blood cell microscope images) are shown to demonstrate that ELM can be suitable for biomedical image classification. We exploit different networks: the first one is the classic ELM, and the other ones are: weighted extreme learning machines (WELMs), regularized extreme learning machines (RELMs), and multilayer extreme learning machines. Moreover, we compare the models obtained with the ELM model by using different supervised machine learning algorithms: the first one is the AlexNet model [25], a CNN architecture that is really widespread in biomedical image classification [26; 27; 28], Random Forest, REPTree, Bayes, and Naive Bayes. To the best of the authors' knowledge, this paper represents the first attempt to consider four different ELM models in the context of biomedical image classification. As a matter of fact, the current state-of-the-art literature includes several research papers exploring the adoption of ELM with biomedical images (for instance, in reference [29], the authors consider ELM for the classification of benign and malignant cells in breast cancer, and in [30], ELM is exploited for the diagnosis of COVID-19); however, this represents the first attempt to compare four different ELM architectures with two different bioimages. This paper proceeds as follows: in the next section, the proposed method for biomedical image classification by exploiting extreme learning machines is presented, the four case studies (belonging to two different biomedical image domains) are presented in Section 4 and, finally, in the last section, conclusions and future research plan are drawn.