Microalgae Classification Using Improved Metaheuristic Algorithm

Abstract

Microalgae are present at all levels of nutrients and food networks, so in aquatic environments they are an important part of the food chain of aquatic organisms, which also play an important role as biological purifiers of water resources and regulation. They also affect the pH of the environment; also plants are the only organisms capable of synthesizing long-chain fatty acids. Therefore, microalgae are the supplier and primary source of unsaturated fatty acids (PUFA) for all organisms present in the food chain of the aquatic environment. It should be noted that many microalgae are also biological indicators of water and reflect the ecological status of the environment. Precise classification of microalgae is related to the human observation capability. The present study proposes a new optimized classification technique with higher accuracy to provide a computer-aided classification of the microalgae. The method begins with an image segmentation to determine the region of interest. The segmentation part has been optimized by a new metaheuristic to provide higher accuracy. Then, the features have been extracted and fed to a Support Vector Machine (SVM) for final classification. The comparison results of the proposed method with some other methods show that the proposed method with 0.828 Kappa, and 0.342 and 0.855 min and max value of F1, provides the highest accuracy compared to the others.