Convolutional neural network - Support vector machine based approach for classification of cyanobacteria and chlorophyta microalgae groups

Abstract

Microalgae are single-celled organisms that have been extensively utilized in biotechnology, pharmacology and foodstuff in recent years. The description and classification of many existing microalgae groups are carried out with classical methods in a long time and with a remarkably qualified labor force. Deep learning methods have achieved success in many fields are applied to the classification of microalga groups. In this study, Cyanobacteria and Chlorophyta microalga groups images are captured by using an inverted microscope. Data augmentation process has been carried out to increase the classification success in Convolutional Neural Network (CNN) models. The collected images are classified by employing two different methods. For the first method, classification is performed with seven different CNN models. In the second method, the Support Vector Machine (SVM) is used to increase the classification success of the AlexNet model with the lowest accuracy. For this, deep features which are extracted from the AlexNet model are classified with SVM. Four different kernel functions are used in the SVM classification process. The highest accuracy is found to be 99.66% among the different CNN models. AlexNet, which has the lowest accuracy with 98%, has reached 99.66% accuracy as a result of its application with SVM.