A 3d convolutional neural network for bacterial image classification

T. S.R. Mhathesh, J. Andrew, K. Martin Sagayam, Lawrence Henesey

Research output: Chapter in Book/Report/Conference proceedingConference contribution

24 Citations (Scopus)

Abstract

Identification and analysis of biological microscopy images need high focus and years of experience to master the art. The rise of deep neural networks enables analyst to achieve the desired results with reduced time and cost. Light sheet fluorescence microscopies are one of the types of 3D microcopy images. Processing microscopy images is tedious process as it consists of low-level features. It is necessary to use proper image processing techniques to extract the low-level features of the biological microscopy images. Deep neural networks (DNN) are efficient in extracting the features of images and able to classify with high accuracy. Convolutional neural networks (CNN) are one of the types of neural networks that can provide promising results with less error rates. The ability of CNN to extract the low-level features of images makes it popular for image classification. In this paper, a CNN-based 3D bacterial image classification is proposed. 3D images contain more in-depth features than 2D images. The proposed CNN model is trained on 3D light sheet fluorescence microscopy images of larval zebrafish. The proposed CNN model classifies the bacterial and non-bacterial images effectively. Intense experimental analyses are carried out to find the optimal complexity and to get better classification accuracy. The proposed model provides better results than human comprehension and other traditional machine learning approaches like random forest, support vector classifier, etc. The details of network architecture, regularization, and hyperparameter optimization techniques are also presented.

Original languageEnglish
Title of host publicationIntelligence in Big Data Technologies—Beyond the Hype - Proceedings of ICBDCC 2019
EditorsJ. Dinesh Peter, Steven L. Fernandes, Amir H. Alavi, Amir H. Alavi
PublisherSpringer Gabler
Pages419-431
Number of pages13
ISBN (Print)9789811552847
DOIs
Publication statusPublished - 2021
Event3rd International Conference on Big-Data and Cloud Computing, ICBDCC 2019 - Coimbatore, India
Duration: 06-12-201907-12-2019

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1167
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

Conference3rd International Conference on Big-Data and Cloud Computing, ICBDCC 2019
Country/TerritoryIndia
CityCoimbatore
Period06-12-1907-12-19

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • General Computer Science

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