# Automated Diatom Classification (Part B): A Deep Learning Approach
## 1 Introduction
Models such as _AlexNet_ ([6], winner in ImageNet Large Scale Visual Recognition Competition (ILSVRC) 2010 and 2012) and _GoogLeNet_ ([7], winner in ILSVRC 2014) have achieved impressive results in the past and have become state of art models for this reason. To summarize, different classification techniques using different features have been presented in the literature to classify diatom types, ranging between 14 to 80 species, with databases not larger than 30,000 samples. The maximum overall accuracy obtained is 98.1%. Table 1 presents a summary of these works together with the proposed one. Table 1 shows the number of diatom types (Num. Species), the total number of samples (Num. Samples), the classification method and the results. In this study, we do a deep analysis of CNN techniques with a significant database composed of a maximum of 160,000 brightfield samples. The raw data is composed by nearly 11,000 diatom \begin{table}
\begin{tabular}{c c c c c c} \hline
**Year [Reference]** & **Num. Species** & **Num. Samples** & **Num. of Features and Type** & **Classifier** & **Accuracy (Vis)** \\ \hline
2002 [8] & 37 & 781 &
\begin{tabular}{c} 321 from Geometrical, Textural, \\ Morphological and Frequency \\ \end{tabular} & Bagging Tree & 96.9 \\ \hline
2003 [9] & 1 & 66 & 10 Morphological & Multiple Discriminant Analysis & 80.3 \\ \hline
2012 [5] & 38 & 837 & 30 Morphological and 200 Texture & Random forest & _97.97_ \\ \hline  & 48 & 1019 & 30 Morphological and 200 Texture & Random forest & 97.15 \\ \hline  & 55 & 1098 & 30 Morphological and 200 Texture & Random forest & 96.17 \\ \hline
2016 [10] & 14 & 10,000 & 4 Geometrical 2 Moments and 33 Morphological & SVM & 94.7 \\ \hline
2017 [4] & 80 & 24,000 & 273 Morphological, Statistical, Textural, Space-Frequency & Bagging Tree & 98.1 \\ \hline Proposed & 80 & 24,000 & CNN-AlexNet & Softmax & 95.62 \\ \hline Proposed & 80 & 160,000 & CNN-AlexNet & Softmax & 99.51 \\ \hline \end{tabular}
\end{table} 
Table 1: Related work and results summary. samples labeled by the expert. After the image processing workflow and the first iteration of the data augmentation, 69,350 samples are available. Further iterations of the data augmentation process are performed, using rotations of 2\({}^{\circ}\), to obtained the number of samples stated before. Additional datasets have been built applying some common preprocessing techniques such as segmentation or normalization over the raw data. These datasets are described in Section 2. In Section 3 the deep learning approach is described and the results are shown in Section 4, the performance ranges between 95.62% to 98.81%. For the first accuracy, we used a segmented database composed of 300 samples, the same database used in the previous study (previous article [4]). The better accuracy is obtained with an augmented version of the dataset, only applied in this method, since [4] states that the accuracy in traditional methods do not improve with augmented data (it is also counterproductive due to orientation-invariant features and overfitting). Using augmented and combined datasets results improve up to 99.51%. There, a dataset composed by non-treated and normalized images, limited to 2000 samples per species is used. Finally, conclusions are presented in Section 5.