# Automated Diatom Classification (Part B): A Deep Learning Approach
## 4 Results and Discussion
### Original + Normalized Dataset  
This dataset, as it was explained, is proposed to determine whether a combination of images with different preprocessing can make the model more robust. The results states that this has some impact, but the main weight in accuracy still depends heavily on the number of samples. When the balanced training process has 1000 samples per class, similar values in accuracy are obtained, and this value is even increased when 2000 samples are used.  
The confusion matrices for training with 300, 700, 1000 and 2000 samples per class are presented in Figures 18-21, respectively.  
Figure 17: Normalized dataset main confusions (part 2). Each row shows an example of the true species, a missclassifed sample between two species and a sample of the second one are shown.  
Figure 18: Confusion matrix heat map for the merged dataset with 300 samples per class.  
In terms of misclassified species, this dataset behaves in the same way as the rest, stating that even with images from different sources in terms of different processing techniques the learned model is not affected, making it quite robust in comparison with other methods.  
Figure 19: Confusion matrix heat map for the merged dataset with 700 samples per class.  
Figure 20: Confusion matrix heat map for the merged dataset with 1000 samples per class.  
Table 7 shows a summary of the experiments done with this database, giving precise information about the dataset, the number of samples per class, the mean accuracy of the 10 folds from cross-validation and standard deviation. The accuracy for testing has been calculated as the percentage of labels that match between the ground truth and the classifier. Additionally, Table 8 show the top-30 species that are missclassified, with the sensitivity achieved with this model.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Dataset** & **Samples per Class** & **Mean Accuracy (\%)** & **Standard Deviation** \\ \hline Original + Normalized & 300 & 92.69 & 0.41 \\ Original + Normalized & 700 & 96.91 & 0.25 \\ Original + Normalized & 1000 & 98.22 & 0.17 \\ Original + Normalized & 2000 & **99.51** & 0.048 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Deep Learning experiments summary.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Class** & **Sensitivity** & **Class** & **Sensitivity** \\ \hline Encyonopsis alpina & 0.93 & Navicula crypbotenella & 0.99 \\ Eolimna minima & 0.96 & Nitzschia fossilis & 0.99 \\ Achanthidium rivulare & 0.97 & Rhoicosphenia abbreviata & 0.99 \\ Encyonema reichardtii & 0.975 & Achnanthes subhudsonis & 0.995 \\ Nitzschia amphibia & 0.98 & Achnanthidium atomoides & 0.995 \\ Achanthidium catenatum & 0.985 & Achnanthidium eutrophilum & 0.995 \\ Encyonopsis minuta & 0.985 & Achnanthidium jackii & 0.995 \\ Gombonema insigniforme & 0.99 & Cyclostephanos dubius & 0.995 \\ Achnanthidium exile & 0.99 & Cyclotella atomus & 0.995 \\ Amphora pediculus & 0.99 & Eolimna rhombelliptica & 0.995 \\ Cymbella excisa var excisa & 0.99 & Epithemia adnata & 0.995 \\ Gombonema angustivalva & 0.99 & Fragilaria gracilis & 0.995 \\ Gombonema micropus & 0.99 & Gombonema pumilum var elegans & 0.995 \\ Gombonema minutum & 0.99 & Navicula crypbotenelloides & 0.995 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Sensitivity of top-30 error-prone species.  
Figure 21: Confusion matrix heat map for the merged dataset with 2000 samples per class.