# Experimenting with Extreme Learning Machine for Biomedical Image Classification
## 4 Experimental Analysis
### The Datasets  
In order to evaluate the ELM adoption in biomedical image classification tasks, we resort to two different datasets belonging to the MedMNIST [[https://medmnist.com/](https://medmnist.com/)]([https://medmnist.com/](https://medmnist.com/))[37] (accessed on 20 July 2023) repository, a collection of standardized biomedical images from different organs and acquired with different instruments. All images are pre-processed into 28 \(\times\) 28 (i.e., 2D) with the corresponding classification labels. In particular, we exploit biomedical acquires with two different biomedical equipment: dermatoscope and blood cell microscope, with the aim of showing that ELM can be generally exploited for (multiclass and binary) biomedical image classification tasks. For each classification, a k-fold cross-validation process is exploited. K-fold cross-validation is a common technique used in machine learning to assess the performance and generalization ability of a model. It involves partitioning the dataset into K subsets or folds of approximately equal size, where K is a predefined integer value. The training and evaluation process is then repeated K times, with each fold serving as the validation set once, while the remaining K-1 folds are used for training. In particular, we set k = 10. The first dataset we take into account is the DermaMNIST one: it is based on the HAM10000 [38; 39], a large collection of multi-source dermatoscopic images of common pigmented skin lesions. The DermaMNIST dataset comprises 10,015 dermatoscopic images, out of which 8010 images are designated for training purposes, while the remaining 2005 images are allocated for testing. The dataset includes images classified into seven distinct diseases. To divide the dataset into training and testing sets, an 80:20 ratio is employed. The original source images, which have dimensions of 3 \(\times\) 600 \(\times\) 450, are resized to 3 \(\times\) 28 \(\times\) 28. This resizing results in each image having dimensions of 28 \(\times\) 28 with three RGB channels. In particular, the DermaMNIST dataset is composed by the following labels (exploited in the multiclass classification) to discriminate between the different diseases affecting the Derma:  
* The 0 label is related to actinic keratoses and intraepithelial carcinoma;
* The 1 label is related to basal cell carcinoma;
* The 2 label is related to benign keratosis-like lesions;
* The 3 label is related to dermatofibroma;
* The 4 label is related to melanoma;
* The 5 label is related to melanocytic nevi;* The 6 label is related to vascular lesions. With regard to the DermaMNIST binary classification, we consider the two following labels:
* The 0 label is related to following diseases, including actinic keratoses and intraepithelial carcinoma, basal cell carcinoma, benign keratosis-like lesions, dermatofibroma, melanocytic nevi, and 'vascular lesions';
* The 1 label is related to melanoma (this case study is related to melanoma disease detection). The second dataset we consider is the BloodMNIST one, which belongs to the MedMINST collection as the DermaMinst dataset. The BloodMNIST dataset [40] is derived from a collection of individual normal blood cells obtained from individuals who do not have any infections, hematologic or oncologic diseases, and are not undergoing any pharmacologic treatment at the time of blood collection. The dataset consists of a total of 17,092 images, among which 13,671 images are used for training the model, while the remaining 3412 images are reserved for model testing. The dataset is categorized into eight different classes. To divide the source dataset into training and testing sets, an 80:20 ratio is employed. The source images, initially with a resolution of \(3\times 360\times 363\) pixels, are first center-cropped to \(3\times 200\times 200\) pixels. Subsequently, they are resized to \(3\times 28\times 28\) pixels. This resizing results in images with a width and height of 28 pixels and each pixel is represented in three channels (RGB format).