# Experimenting with Extreme Learning Machine for Biomedical Image Classification
## 4 Experimental Analysis
### Multiclass Classification Results  
In the following, we describe the classification process and results we obtained with the multiclass classification, respectively, with the DermaMINST and the BloodMINST dataset. This third case study is related to the classification of different pathologies afflicting the Derma detection with the DermaMINST dataset: 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 0 is related to actinic keratoses and intraepithelial carcinoma, 1 denotes 'basal cell carcinoma, 2 denotes benign keratosis-like lesions, 3 denotes dermatofibroma, 4 denotes melanoma, 5 denotes'melanocytic nevi, and 6 denotes vascular lesions. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F-Measure** & **Accuracy** & **Time** \\ \hline J48 & 0.990 & 0.990 & 0.990 & 0.990 & 0:00:32.89 \\ \hline Random Forest & 0.997 & 0.997 & 0.997 & 0.997 & 0:00:23.12 \\ \hline REPTree & 0.989 & 0.989 & 0.989 & 0.989 & 0:00:9.08 \\ \hline Bayes & 0.980 & 0.977 & 0.978 & 0.978 & 0:00:21.08 \\ \hline Naive Bayes & 0.981 & 0.979 & 0.980 & 0.980 & 0:00:2.78 \\ \hline AlexNet & 0.991 & 0.991 & 0.990 & 0.991 & 0:18:45.48 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: BloodMINST binary machine learning classification results. Figure 4: The BloodMINST binary classification confusion matrix. Table 5 shows the results obtained in the DermaMINST multiclass classification with the ELM, W-ELM, R-ELM, and ML-ELM models. As shown from the results shown in Table 5, the accuracy ranges from 0.670 to 0.683. This happens for all the ELM models considered. Figure 5 shows the confusion matrix for the third case study. The DermaMINST multiclass classification confusion matrix for the ELM model (trained with 1000 hidden neurons) is shown in Figure 5.  
From the confusion matrix in Figure 5, we can observe that a large part of the derma images is correctly classified in the right class, thus confirming the effectiveness of ELM. Table 6 shows the results we obtained in comparison with machine learning in the DermaMINST multiclass classification. From the classification results in Table 6, it can be seen that the ELM models are able to obtain (slightly) better performances if compared to the AlexNet (able to reach an accuracy equal to 0.668). The ELM model requires 23 s for the training and testing of the model, while the AlexNet needs 26 min for the same task. Moreover, with regard to J48, Random Forest, REPTree, Bayes, and Naive Bayes models, an accuracy ranging from 0.31 to 568 is  
Figure 5: The DermaMINST multiclass classification confusion matrix. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Alg** & **hn** & **Precision** & **Recall** & **F-Measure** & **Accuracy** & **Time** \\ \hline \multirow{3}{*}{ELM} & 50 & 0.520 & 0.670 & 0.554 & 0.670 & 0.00:1.78 \\  & 100 & 0.562 & 0.677 & 0.578 & 0.677 & 0.00:1.08 \\  & 500 & 0.590 & 0.683 & 0.609 & 0.683 & 0.00:1.72 \\  & 1000 & 0.588 & 0.674 & 0.614 & 0.674 & 0.00:3.07 \\ \hline \multirow{3}{*}{W-ELM} & 50 & 0.526 & 0.670 & 0.554 & 0.670 & 0.00:1.10 \\  & 100 & 0.597 & 0.677 & 0.586 & 0.677 & 0.00:1.32 \\  & 500 & 0.593 & 0.681 & 0.609 & 0.681 & 0.00:2.44 \\  & 1000 & 0.593 & 0.678 & 0.620 & 0.678 & 0.00:4.48 \\ \hline \multirow{3}{*}{R-ELM} & 50 & 0.532 & 0.668 & 0.554 & 0.668 & 0.00:0.91 \\  & 100 & 0.535 & 0.670 & 0.567 & 0.670 & 0.00:0.94 \\  & 500 & 0.570 & 0.674 & 0.596 & 0.674 & 0.00:1.19 \\  & 1000 & 0.580 & 0.665 & 0.604 & 0.665 & 0.00:1.40 \\ \hline \multirow{3}{*}{ML-ELM} & 50 & 0.542 & 0.668 & 0.553 & 0.668 & 0.00:1.49 \\  & 100 & 0.608 & 0.679 & 0.580 & 0.679 & 0.00:1.51 \\ \cline{1-1}  & 500 & 0.622 & 0.687 & 0.611 & 0.687 & 0.00:2.43 \\ \cline{1-1}  & 1000 & 0.605 & 0.683 & 0.621 & 0.683 & 0.00:4.43 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: DermaMINST multiclass ELM classification results.