An Optimal Inception Based Segmentation and Classification Model for Diatom Species Images
A. Victoria Anand Mary1, G. Prabakaran2
1A. Victoria Anand Mary , Research Scholar, Department of Computer Science and Engineering, Annamalai University, India.
2G. Prabakaran, Assistant Professor, Department of Computer Science and Engineering, Annamalai University, India.
Manuscript received on 19 August 2019. | Revised Manuscript received on 24 August 2019. | Manuscript published on 30 September 2019. | PP: 6331-6345 | Volume-8 Issue-3 September 2019 | Retrieval Number: C6047098319/19©BEIESP | DOI: 10.35940/ijrte.C6047.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Diatom; Segmentation; Classification; Deep Learning.
Scope of the Article: Classification