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End-to-end Convolutional Neural Network Design for Automatic Detection of Influenza Virus
Junghwan Lee,Heesang Eom,Yuli Sun Hariyani,Cheonjung Kim,Yongkyoung Yoo,Jeonghoon Lee,Cheolsoo Park 대한전자공학회 2021 IEIE Transactions on Smart Processing & Computing Vol.10 No.1
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.
Detection of Arrhythmia using 1D Convolution Neural Network with LSTM Model
Seungwoo Han,Wongyu Lee,Heesang Eom,Juhyeong Kim,Cheolsoo Park 대한전자공학회 2020 IEIE Transactions on Smart Processing & Computing Vol.9 No.4
Considering the high death rate from cardiovascular diseases, it is important to detect an irregular heart rhythm in order to prevent potential tragedy. The purpose of this paper is to present automatic detection of arrhythmia based on electrocardiography. We suggest a one-dimensional convolutional neural network (1D CNN) with long short-term memory (LSTM). The suggested architecture is compared with two other deep learning methods: the 1D CNN and the multi-layer perceptron (MLP) model. To evaluate performance, we measured the overall accuracy, macroaveraged precision, and macro-averaged recall of our proposed method as being 92.03%, 90.98%, and 86.15%, respectively. The results demonstrate that the 1D CNN-with-LSTM model outperforms the two other models.