# Multi-Input Convolutional Neural Networks for Automatic Pollen Classification
## 3 Results and Discussions
### Classification Results  
In this section, we evaluate the results of the trained model and compare to the previous work performed on this type of classification for all four datasets. In Table 5, the results of our trained network are presented in comparison to the results from [8].  
The results presented in Table 5 show an improvement over the baseline [8] on all datasets, with the highest increase in accuracy for the SAU-LI dataset. The results analysis can be split into two parts based on the usage of features. SAU-SRB and SAU-CH used all three feature types and can be compared directly with the results from this work. As presented in Table 5, our approach improved over the current best. In respect to SAU-SRB, we reduced the error rate from 26% to 23% and, compared to SAU-CH, from 20% to 15%. The greatest improvement, obtained in respect to the SAU-CH dataset, is in part due to the lower number of classes in this dataset.  
When comparing with the results on the SAU-LI dataset, extra care has to be taken into account as the original paper only used two of the three feature types (i.e., spectrum and scattering). In our approach, all three features were used, leading to the best improvement compared to the other datasets. An improvement is still present even if we use just the two features, as in the original work. This was performed by multiplying with zeros the lifetime feature and doing a separate training and testing run. In this comparison, we obtained an accuracy of 78% by using just the two features. The takeaway message is that having  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Hyperparameter** & **Scattering** & **Spectrum** & **Lifetime** \\ \hline learning rate & 0.001 & 0.0001 & 0.0001 \\ \hline batch size & 175 & 248 & 112 \\ \hline size dense layer & 362 & 718 & 614 \\ \hline drop-out rate dense layer & 0.3 & 0.8 & 0.6 \\ \hline filter size & 5 & 5 & 3 \\ \hline \# of conv. filters (first layer) & 28 & 14 & 108 \\ \hline \# of conv. layers/cells & 3 & 2 & 1 \\ \hline \# of cells & 2 & 1 & 3 \\ \hline \# symbol is used in place of number. & & & \\ \end{tabular}
\end{table}
Table 4: Best hyperparameters. Hyperparameter tuning was performed independently for each FE; thus, the best configuration of hyperparameters is shown independently for each one.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **SAU-SRB** & **SAU-Li** & **SAU-CH** & **MARS** \\ \hline Baseline 1  & 74\% & 73\% & 80\% & - \\ \hline Initial architecture 2  & 77\% & 84\% & 85\% & 76\% \\ \hline Scattering image only & 53\% & 60\% & 57\% & 59\% \\ \hline Fluorescence spectrum only & 49\% & 70\% & 45\% & 50\% \\ \hline Lifetime signal only & 60\% & 57\% & 67\% & 39\% \\ \hline \end{tabular}
\end{table}
Table 5: Model performance (accuracy).  
more features improves classification even though these extra features appear noisy to the human eye.  
In Table 5, rows 3-5 show the results obtained by performing the classification with one feature type at a time. This was possible thanks to the added intermediary outputs after each feature extractor. The results show that the most important feature depends on the dataset and on the relationship among the pollen classes in the dataset. For example, if we have many classes of pollen that have similar size and shape, the scattering image is less useful for classification (see dataset SAU-SRB, for example). Another important thing is that the features appear to be complementary, the combined results of the classification being well above the performance of any individual feature extractor.