| Title |
End-to-end Convolutional Neural Network Design for Automatic Detection of Influenza Virus |
| Authors |
(Junghwan Lee) ; (Heesang Eom) ; (Yuli Sun Hariyani) ; (Cheonjung Kim) ; (Yongkyoung Yoo) ; (Jeong Hoon Lee) ; (Cheolsoo Park) |
| DOI |
https://doi.org/10.5573/IEIESPC.2021.10.1.031 |
| Keywords |
Influenza kit; BOHB; Deep learning |
| Abstract |
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. |