# Experimenting with Extreme Learning Machine for Biomedical Image Classification
## 4 Experimental Analysis
### Multiclass Classification Results
obtained, also confirming for these models that ELM networks are able to obtain (slightly) better performances for the DermaMINST multiclass classification. The last case study we present is related to multiclass classification with the Blood-MINST dataset. The idea of this fourth case study is the identification of different blood cells: to this aim, 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, 2, 3, 4, 5, 6, 7}, where the label 0 denotes basophil, the label 1 denotes eosinophil, the label 2 denotes erythroblast, the label 3 denotes immature granulocytes, the label 4 denotes lymphocyte, the label 5 denotes monocyte, the label 6 denotes neutrophil, and the label 7 denotes platelet. The results related to the fourth case study, i.e., the BloodMINST multiclass classification with ELM, W-ELM, R-ELM, and ML-ELM models, are shown in Table 7.  
As shown from Table 7, in this last case study, it can be seen that the accuracy obtained ranges from 0.502 to 0.697 for the ELM, W-ELM, R-ELM, and ML-ELM models. In particular, quite satisfactory accuracy is obtained from the ELM model, trained with 1000 hidden neurons, equal to 0.697. Figure 6 shows the confusion matrix for the third case study when the ELM model with 1000 hidden neurons is considered. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F-Measure** & **Accuracy** & **Time (s)** \\ \hline J48 & 0.567 & 0.569 & 0.568 & 0.568 & 0:00:26.66 \\ \hline Random & & & & & \\ Forest & 0.512 & 0.684 & 0.515 & 0.514 & 0:00:15.78 \\ \hline REPTree & 0.559 & 0.663 & 0.589 & 0.568 & 0:00:6.32 \\ \hline Bayes & 0.608 & 0.225 & 0.244 & 0.312 & 0:00:2.81 \\ \hline Naive Bayes & 0.611 & 0.220 & 0.246 & 0.327 & 0:00:2.11 \\ \hline AlexNet & 0.655 & 0.317 & 0.425 & 0.668 & 0:26:00.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: DermaMINST multiclass machine learning classification results. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Alg** & **hn** & **Precision** & **Recall** & **F-Measure** & **Accuracy** & **Time** \\ \hline  & 50 & 0.490 & 0.502 & 0.463 & 0.502 & 0:00:1.45 \\ ELM & 100 & 0.572 & 0.572 & 0.549 & 0.572 & 0:00:1.96 \\  & 500 & 0.660 & 0.661 & 0.652 & 0.661 & 0:00:2.50 \\  & 1000 & 0  
As shown from the BloodMINST multiclass classification, we can note that a large number of instances are correctly classified in the right cell class, confirming the effectiveness of ELM in blood cell identification. Table 8 shows the comparison with respect to machine learning for the BloodMINST multiclass classification. From Table 8, it can be seen that the ELM model obtains a better accuracy than the AlexNet model, which is able to reach an accuracy of 0.269, with an amount of time equal to 39 s for training and testing with the ELM model and equal to 26 s for AlexNet. Relating to the remaining models (i.e., J48, Random Forest, REPTree, Bayes, and Naive Bayes), an accuracy ranging from 0.623 to 0.755 is reached; therefore, we note that, in this last case study, in some cases, slightly higher accuracy values are reached compared to the ELM models: in any case, we note that the training time is always less for the ELM models. As seen from the experimental results in the four case studies, the ELM model is able to obtain even better accuracy than the AlexNet network in all the case studies, which also applies to most of the machine learning models considered in this experiment, allowing significantly reduced times for the model training and testing phases.