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Open AccessArticle

Automatic Zebrafish Egg Phenotype Recognition from Bright-Field Microscopic Images Using Deep Convolutional Neural Network

1
School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, No.2 Linggong Street, Dalian 116024, China
2
College of Environmental Science and Engineering, Biomedical Multidisciplinary Innovation Research Institute, Shanghai Institute of Pollution Control and Ecological Security, Key Laboratory of Yangtze River Water Environment, Ministry of Education, Tongji University, 1239 Siping Road, Shanghai 200092, China
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2019, 9(16), 3362; https://doi.org/10.3390/app9163362
Received: 9 June 2019 / Revised: 4 July 2019 / Accepted: 8 July 2019 / Published: 15 August 2019
(This article belongs to the Special Issue Intelligent Processing on Image and Optical Information)
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PDF [3178 KB, uploaded 15 August 2019]
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Abstract

Zebrafish eggs are widely used in biological experiments to study the environmental and genetic influence on embryo development. Due to the high throughput of microscopic imaging, automated analysis of zebrafish egg microscopic images is highly demanded. However, machine learning algorithms for zebrafish egg image analysis suffer from the problems of small imbalanced training dataset and subtle inter-class differences. In this study, we developed an automated zebrafish egg microscopic image analysis algorithm based on deep convolutional neural network (CNN). To tackle the problem of insufficient training data, the strategies of transfer learning and data augmentation were used. We also adopted the global averaged pooling technique to overcome the subtle phenotype differences between the fertilized and unfertilized eggs. Experimental results of a five-fold cross-validation test showed that the proposed method yielded a mean classification accuracy of 95.0% and a maximum accuracy of 98.8%. The network also demonstrated higher classification accuracy and better convergence performance than conventional CNN methods. This study extends the deep learning technique to zebrafish egg phenotype classification and paves the way for automatic bright-field microscopic image analysis. View Full-Text
Keywords: zebrafish egg; microscopy image processing; convolutional neural network zebrafish egg; microscopy image processing; convolutional neural network
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Shang, S.; Long, L.; Lin, S.; Cong, F. Automatic Zebrafish Egg Phenotype Recognition from Bright-Field Microscopic Images Using Deep Convolutional Neural Network. Appl. Sci. 2019, 9, 3362.

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