JISE


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Journal of Information Science and Engineering, Vol. 37 No. 4, pp. 827-838


Anomaly Chicken Cell Identification Using Deep Learning Techniques

NATINAI JINSAKUL1,2, CHENG-FA TSAI2,* AND CHIA-EN TSAI3
1Department of Tropical Agriculture and International Cooperation
2Department of Management Information Systems,
National Pingtung University of Science and Technology
Pingtung, 912 Taiwan
3Department of Biochemistry and Molecular Biology
National Cheng Kung University
Tainan, 701 Taiwan
E-mail: {P10522009; cftsai}@mail.npust.edu.tw; {plusntsai}@gmail.com

Chicken cell abnormal identification by manual method that clearly lacks speed and accuracy. However, the success of deep learning techniques from the convolutional neural network (CNN), it may be providing solutions to cell biology laboratory tasks. This paper collected the novel chicken cell microscopic image datasets for training the different kinds of CNN models and optimizers to find promising applications that might be developed. The top model indicates that ResNet34 with Adam optimizer achieved training accuracy of 100%, testing accuracy of 98.14%, and the lower time on the outstanding confusion matrix. In addition, the validation result represented correct identification, guaranteeing by experts. This study shows the potential method to be improved to an application of identi-fication systems in the actual animal and biology laboratories.

Keywords: anomaly identification, chicken cell, microscopic image, artificial intelligence, deep learning, convolutional neural networks, optimizers

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