An optimal inception based segmentation and classification model for diatom species images

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Diatoms act an essential contributor to the fundamental creation in aquatic ecosystem, which is positioned at the foundation of the food chain. Presently, the diatoms appear as a most important topic over the globe in studies interrelated to weather changes, and in the design of functions which enables to model of those variations. In addition, it is an efficient indicator of ecological conditions and is widely employed in water quality assessment. In a similar way, deep learning model is a widely employed technique for classifying images among diverse applications. In this paper, an optimal segmentation and classification model for diatom images particularly species images. Here, edge detection based segmentation model is employed for segmenting the images and then Inception model is utilized for classifying images. A detailed simulation process takes place on the benchmark diatom images. An overall accuracy of 99 is attained by the presented model on the applied set of test images. The outcome is compared to the state of art classification models and the results exhibited the superior performance of the presented model.




Victoria Anand Mary, A., & Prabakaran, G. (2019). An optimal inception based segmentation and classification model for diatom species images. International Journal of Recent Technology and Engineering, 8(3), 6331–6345.

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