# Experimenting with Extreme Learning Machine for Biomedical Image Classification
## 4 Experimental Analysis
### Binary Classification Results  
In this section, we report the results we obtained in the binary classification task by exploiting both the DermaMINST and the BloodMNIST datasets. In particular, the first binary model is related to the melanoma detection: we consider \(T_{detection}\) as a set of labels {(\(M_{detection}\), \(l_{detection}\))}, where each \(M_{detection}\) is the label that is associated with a \(l_{detection}\)\(\in\) {0, 1}, where 1 denotes melanoma, while 0 denotes other pathologies (i.e., actinic keratoses, intraepithelial carcinoma, basal cell carcinoma, benign keratosis-like lesions, dermatofibroma, melanocytic nevi, and vascular lesions). We build a numeric vector of features \(\text{F}\in R_{y}\), where \(y\) represents the feature number exploited in the learning phase (\(y=784\), as a matter of fact, for each 28 \(\times\) 28 gray-scale image under analysis, we consider each pixel as a feature). Table 1 shows the classification results obtained from binary classification with the DermaMINST dataset with the ELM, WELM, ELM, and MELM models. For each ELM model, we consider four different hidden neurons (i.e., column hn in Table 1): 50, 100, 500, and 1000. We also report the time required for the classification (i.e., column Time in Table 1). As shown in Table 1, all the ELM models are able to reach interesting performances, very similar to each other. For instance, the ELM model with 1000 hidden neurons obtains an accuracy equal to 0.884, while with 50 hidden neurons, the accuracy is equal to 0.888. Also, the W-ELM, R-ELM, and ML-ELM models obtain an accuracy ranging from 0.887 to 0.891, by confirming the effectiveness of ELM in the task of the binary classification of the DermaMINST dataset. The confusion matrix is shown in Figure 3 for DermaMinst binary classification with the ELM model. From the confusion matrix, it emerges that the ELM model is able to correctly classify 32 images related to melanoma and 1742 images related to other pathologies affecting the Derma. In Table 2, we show the results obtained with the machine learning algorithms we exploited for comparison. As shown from Table 2, the ELM models are able to obtain better performances with respect to the AlexNet one: as a matter of fact, the ELM model reaches an accuracy equal to 0.872, while the AlexNet model obtains an accuracy of 0.343. Moreover, the ELM model requires 33 s for training, while the AlexNet one more than 13 min. This also happens  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Alg** & **hn** & **Precision** & **Recall** & **F-Measure** & **Accuracy** & **Time** \\ \hline \multirow{4}{*}{ELM} & 50 & 0.846 & 0.888 & 0.838 & 0.888 & 0:00:1.55 \\  & 100 & 0.831 & 0.887 & 0.839 & 0.887 & 0:00:1.56 \\  & 500 & 0.8  
when we consider the J48, the Random Forest, the REPTree, the Bayes, and the Naive Bayes classification algorithm: in fact, an accuracy ranging from 0.828 to 0.864 is obtained with these models, thus showing that ELM models obtain better accuracy in this case study. In the next, we describe the classification process and results we obtained with the binary classification with the BloodMINST dataset. This second case study is related to platelets' detection: in this case, we consider \(T_{detection}\) as a set of labels {\((M_{detection},l_{detection})\)} where each \(M_{detection}\) is the label that is associated with a \(l_{detection}\in\{0,1\}\), where \(1\) is related to platelets, while \(0\) with the other blood cells (i.e., basophil, eosinophil, erythroblast, immature granulocytes, lymphocyte, monocyte, and neutrophil). In Table 3, we show the results of the binary classification performed with the BloodMINST dataset. From Table 3, it can be seen that ELM, W-ELM, R-ELM, and ML-ELM models are able to reach good performances, as a matter of fact, the accuracy is ranging from 0.888 to 0.997: in this case, the best result (i.e., 0.997) is obtained from the R-ELM model, when 500 and 1000 hidden neurons are used.