Automatic cell image classification with convolutional neural networks

被引：0
|
作者：
Kim S.-H. [1 ]
Lee J.-H. [1 ]
Choi E.-Y. [1 ]
Jeon S.-T. [1 ]
Choi M.-Y. [1 ]
Jo S.-H. [1 ]
Choe S.-W. [2 ]
机构：
[1] Dept. of Medical It Convergence Engineering, Kumoh National Institute of Technology
[2] Dept. of Medical It Convergence Engineering, Dept. of It Convergence Engineering, Kumoh National Institute of Technology
来源：
Transactions of the Korean Institute of Electrical Engineers | 2021年 / 70卷 / 01期
关键词：
Automatic classification; Ccd-986sk; Convolutional neural network; Deep learning; Hela cell; Opencv;
D O I：
10.5370/KIEE.2021.70.1.139
中图分类号：
学科分类号：
摘要：
Recently artificial intelligence can be used in various fields, especially for medical purposes. For example, it can help diagnose lung diseases and cancer accurately and quickly, thereby reducing the time and cost of medical treatment. In this study, image data were acquired using cultured cervical cancer cells and skin fibroblast cells. The acquired images were pre-processed using OpenCV and enabled the creation of input data optimized for training. In addition, an optimal deep learning algorithm was designed to classify cells by type using transfer learning methods. As a result, the CNN-based learning and automatic classification method proposed in this experiment showed a high accuracy of over 98% and is expected to be used for accurate diagnosis and treatment of diseases in the future. © 2021 Korean Institute of Electrical Engineers. All rights reserved.
引用
收藏
页码：139 / 144
页数：5
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