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• W4309969662 abstract "Recently, there have been studied using Digital Holographic Microscopy (DHM) research and deep learning technology to analyze cells or diagnose diseases. 3D information of microscopic substances can be obtained through DHM, and several diseases can be diagnosed through the analysis of red blood cell (RBC)s in the human body. However, deep learning systems still show diagnostic results with lower accuracy than human judgment and have disadvantages in that more training data and expensive computers are needed to increase the accuracy. To overcome these shortcomings and help human judgment, this study proceeds with a study to automatically display 3D visual information and statistical data of RBC s in a system using 3D imaging technology based on DHM. Through this, depth information and shape data that are difficult to obtain with only two-dimensional information can be obtained. Therefore, using the average and variance values of each parameter as indicators, it is expected to help diagnose the presence or absence of diseases in which shape changes such as expansion or loss of groove height of the RBC occur." @default.
• W4309969662 created "2022-11-30" @default.
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• W4309969662 date "2022-10-19" @default.
• W4309969662 modified "2023-09-29" @default.
• W4309969662 title "A study on the classification of red blood cells specimen and data analysis method for the diagnosis of blood disease using a digital holographic microscopy" @default.
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• W4309969662 doi "https://doi.org/10.1109/ictc55196.2022.9952868" @default.
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