Identification and enumeration of cyanobacteria species using a deep neural network

Abstract

Cell classification and cell counting are essential for the detection, monitoring, forecasting, and management of harmful algae populations. Conventional methods of algae classification and cell counting are known to be time-consuming, labor-intensive, and subjective, depending on the expertise of the observers. The objectives of this study were to classify and quantify five cyanobacteria using the deep learning techniques of a fast regional convolutional neural network (R-CNN) and convolutional neural network (CNN). Water samples taken from the Haman weir of Nakdong River and Baekje weir of the Geum River were observed under the optical microscope. The images captured by the microscope were used to classify cyanobacteria species using the fast R-CNN model. Post-processing of the classified images generated by the model reduced the noises of the cell features, thereby improving the accuracy of the CNN model in quantifying cyanobacteria cells. The distinctive morphological features of the five species were extracted by the fast R-CNN model. This model was able to achieve a reasonable agreement with the manual classification results, yielding average precision (AP) values of 0.929, 0.973, 0.829, 0.890, and 0.890 for Microcystis aeruginosa, Microcystis wesenbergii, Dolichospermum, Oscillatoria, and Aphanizomenon, respectively. The CNN model for the Microcystis species obtained an R2 value of 0.775 and RMSE value of 26 cells for training, and an R2 of 0.854 and RMSE of 23 cells for validation. A minor underestimation and overestimation for a population with <50 cells and >250 cells were observed, respectively, which are due to the overlapping of cells and the presence of blurry regions in the input images. In conclusion, this study was able to demonstrate the reliable performance of cyanobacteria classification and cell counting using deep learning approaches.