Deep learning-based ResNeXt model in phycological studies for future
2020
Yadav, D.P. | Jalal, A.S. | Garlapati, Deviram | Hossain, Kaizar | Goyal, Ayush | Pant, Gaurav
Algae are photosynthetic eukaryotes that may range from unicellular to multicellular forms. Algae have been reported from almost all the ecological systems, including terrestrial, marine, and aquatic ecosystems. The manual classification of algae is a time-consuming method and requires great efforts with expertise due to the numerous families and genera. In the present study, an automated system is developed for the identification and classification of the 16 algal families with a data set of 80,000 images by a modified ResNeXt CNN (Convolution Neural Network) model. Cell differentiation by modified ResNeXt CNN topology is based on cell arrangement and morphological features including area, width, shape, and length of the cell. An experimental result of 99.97% classification accuracy demonstrates the effectiveness of the proposed method. The present investigation may open a new path in the future for the development of a time and a cost-effective, highly sensitive computer-based system for the identification and classification of different algae.
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