# Automated Diatom Classification (Part B): A Deep Learning Approach
## 1 Introduction  
The specific species found in a water reserve constitute a bioindicator of its quality and whether some kind of activities are more suitable or not. This is stated in international reference documents such as the Directive Framework of Water Policy [1], whose aim is to protect, enhance, and control the quality of rivers, lakes, and seas within the European Union. Scientific evidence shows that biotic indices based on diatoms respond effectively to the presence of elements such as heavy metals, as it is mentioned in [2], and this means that they are quite useful for quality assessment. Diatoms are, together with invertebrates, the most widely used organisms in river quality analysis. Numerous studies also support the effectiveness of biological indices based on diatoms to control the ecological status of water in rivers. Diatoms have been also studied as paleoenvironmental markers, since their silica structures make it possible to reconstruct the historical environmental conditions by studying the diatom fossil deposits in lake sediments. Therefore, these organisms allow not only to determine the current quality of water reserves but, as [3] suggests, to infer the quality status and environmental variables that dominated in the past. Variations in temperature, pH or conductivity over centuries can be estimated by studyingthese organisms in sediments, allowing not only to know how climate has affected the studied area, but to state the baseline conditions from which we can set criteria for optimal quality for a water reserve, according to [1]. The application of the most widespread diatom indices often requires a precise level of classification, which involves time and expert training. Moreover, the identification of morphological microstructures and frustule (the series of linking, siliceous bands associated with a valve) discrimination from other elements in the image (fractionated cells or mineral particles) is still unresolved. Some diatoms that were considered the same species for decades, have been now separated into different species and, moreover, the emergence of new species is continuous. The development of tools for automatic diatoms identification and classification that take into account the contour as well as texture information would be helpful in a wide range of applications for both experts and non-experts. However, automatic diatom classification is still an open challenge. Some works have been reported in the literature to automatically classify diatoms. However, they have been based on general state-of-art features, which are limited and maybe not suitable enough for this problem. Moreover, the developed models are only valid for a limited subset of species. Hence, the number of analyzed species has been limited and the results are relatively poor, decreasing the performance when increasing the number of species. The proposed method relies on a technique that builds specific features regarding the input that is provided, keeping the performance when the number of species that are studied grows. The only drawback is the huge amount of data that is necessary to perform the training process, but in this case there is enough available. The best results so far have been presented by the authors in the previous study [4], where 98.1% accuracy is reported for the classification of 80 species with a total of 24,000 segmented samples. Another large dataset with 55 species but with lower number of samples (1093) was presented by [PERSON] et al. [5] with a result of 96.17%. This result improved to 97.97% with less classes (38 diatom types) and 837 samples in total. Regarding the application of deep learning, the difficulty is to build a model able to distinguish among thousands of classes, with an average of 700 image samples per class. Most of the improvements in Convolutional Neural Network (CNN) models, such as inception modules, are aimed at improving accuracy results.