# Improving Foraminifera Classification Using Convolutional Neural Networks with Ensemble Learning
## 1 Introduction  
Image classification is a very complex task that has witnessed massive improvement in the past decade, thanks to hardware advancements and the application of deep learning. Because this new technology is a repetitive and non-creative process, it is well-suited for automation. One of the best deep learners for images is a family of learners called convolutional neural networks (CNNs). CNNs have already proven their efficacy and efficiency at image classification in many studies [1]; however, the stochastic nature of neural networks (NNs) leads to results that are influenced by a fair share of randomness [2]. Ensemble learning (EL) is a widely used implementation that combines results from different heuristic algorithms to achieve better performance and higher consistency, reducing the individual random component of each algorithm. This paper proposes a practical application of EL to the classification problem presented in [3], which was to train a CNN for foraminifera classification, a task of high interest for industrial and research purposes [4]. Species of planktic foraminifera are paleo-environmental bioindicators, whose radiocarbon measurements are used to infer parameters like global ice volume, temperature, salinity, PH, and nutrient content of ancient marine environments. Foraminifera classification is usually performed by groups of humans, ranging in size from 500 to 1000 individuals. As a result, foraminifera classification is a very repetitive, resource-intensive, and time-consuming process. Since the early 1990s, there have been several attempts to automate this task [5]. Although strides were made in this direction, most methods still required strong human supervision. In 2004, however, theneural network SYRACO2 was developed to identify single-celled organisms automatically, and was shown to perform reliably [6]. In 2017 CNNs were applied to diatom identification with great success [7]. With further development, CNNs should be able to take over the arduous process of foraminifera classification. Transfer learning, which takes a pre-trained CNN as the baseline for training on another dataset of images, typically a smaller one, is yet another technique that can be employed to improve classification accuracy at the end of the training process [8]. Our goal is to use these techniques to improve the classification accuracy on the same dataset presented in [3]. This dataset is composed of 1437 groups of sixteen grayscale images of foraminifera taken at different lighting angles and separated into seven classes, six labeled, shown in Figure 1, and one \"other\". Several RGB colorization approaches were used to generate different sets of colored images that became the inputs for a set of CNN architectures that formed the EL environment. The authors of [3] employed a fusion of ResNet50 and Vgg16, using colorization methods based on intensity percentiles in the sixteen grayscale channels. This paper proposes several alternative colorization methods that deviate from the state of the art [9] but still achieve remarkable ensemble results. New approaches such as Luma scaling, HSV color space mapping, and Gaussian or mean-based techniques are applied to CNN architectures to extend the study presented in [3]. The results of this study show that a diverse set of CNN models trained on different colorized images improves the system's performance compared to other state-of-the-art approaches. The proposed system is also shown to outperform human experts in classification accuracy. [PERSON] [PERSON] et al. [3] first proposed this deep-learning-based (DL) framework in 2019, achieving discrete success, but averaging just below the performance of human experts. Their approach did not put much focus on the pre-processing colorization part, missing a potential avenue for improving performance even further. It is important to note that the application of multi-grayscale channels to RGB colorization is not limited to the field of foraminifera classification, but can be applied in other domains as well.