Open AccessAn Efficient Automated Deep Learning Model For Diatom Image Segmentation And Classification
Author(s) -
A. Victoria Anand Mary,
G. Prabakaran
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.k1394.0981119
Subject(s) - artificial intelligence , classifier (uml) , computer science , segmentation , preprocessor , deep learning , image segmentation , pattern recognition (psychology) , contextual image classification , machine learning , image (mathematics)
Recently, diatoms, a type of algae microorganism with numerous species, are relatively helpful for water quality determination, and is treated as an important topic in applied biology nowadays. Simultaneously, deep learning (DL) also becomes an important model applied for various image classification problems. This study introduces a new Inception model for diatom image classification. The presented model involves two main stages namely segmentation and classification. Here, a deep learning based Inception model is employed for classification purposes. To further improve the classifier efficiency, edge detection based segmentation model is also applied where the segmented input is provided as an input to the classifier stage. An experimental validation takes place on diverse set of diatom dataset with various preprocessing models. The results pointed out that the presented DL model shows extraordinary classification performance with a classifier accuracy of 99%.