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Event-based White Blood Cell Classification from Nailfold Capillaries
Yuli Sun Hariyani,Cheolsoo Park 대한전자공학회 2022 IEIE Transactions on Smart Processing & Computing Vol.11 No.4
Detecting absorption gaps in a nailfold capillary can be used to quantify an estimation of white blood cells (WBCs). Previously, the absorption gaps in a nailfold capillary were usually measured using a standard camera on a fingernail. However, difficulties arise due to low visibility of the gap, the small size of the capillaries, the high speed of WBC movement, and the lack of contrast between the capillary and its environment/background. To address these issues, an eventbased WBC image is utilized as input data to detect WBC existence in the nailfold. Specifically, we utilize a dynamic vision sensor (DVS) camera, which can detect a change in luminance on a pixel basis and can produce a stream of asynchronous event output at a microsecond temporal resolution. With the event-based WBC dataset, we conduct a classification task using three different machine learning algorithms: k-nearest neighbors, the decision tree, and random forest. The best result is from random forest with 75.51% accuracy. Based on our evaluation, event-based WBC classification is a promising new approach to detecting WBC presence in nailfold capillaries.
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.