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[학술저널]

  • 학술저널

Junghwan Lee(Kwangwoon University) Heesang Eom(Kwangwoon University) Yuli Sun Hariyani(Kwangwoon University) Cheonjung Kim(Kwangwoon University) Yongkyoung Yoo(Catholic Kwandong University) Jeonghoon Lee(Kwangwoon University) Cheolsoo Park(Kwangwoon University)

DOI : 10.5573/IEIESPC.2021.10.1.031

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Owing to the high mortality rate of influenza diseases, the early examination and accurate detection of the influenza virus are crucial for preventing potential tragedies. This paper reports the design of a highly reliable machine learning classifier for automatic detection of the influenza virus based on an image of its detection kit. Convolutional neural networks (CNNs), currently the most reliable image classifiers, were designed for the images of an influenza detection kit, and their hyperparameters were fine-tuned using an architecture search algorithm, Bayesian optimization, and hyperband (BOHB). With an overall accuracy of 90.14%, the designed and optimized 2DCNNs algorithm successfully separate the influenza virus from normal using the detection kit images.

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Abstract
1. Introduction
2. Background
3. Materials and Methods
4. Results
5. Conclusion
References

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