Engineering Hexagonal Array of Nanoholes for High Sensitivity Biosensor and Application for Human Blood Group Detection

2018 ◽  
Vol 17 (3) ◽  
pp. 475-481 ◽  
Author(s):  
Mohammad Reza Rakhshani ◽  
Mohammad Ali Mansouri-Birjandi
Author(s):  
HUMMAM GHASSAN GHIFARI ◽  
DENNY DARLIS ◽  
ARIS HARTAMAN

ABSTRAKPendeteksian golongan darah dilakukan untuk mengetahui golongan darah yang dimiliki. Hingga saat ini pendeteksian golongan darah masih dilakukan oleh petugas analis kesehatan menggunakan kemampuan mata manusia. Pada penelitian ini dilakukan perancangan alat pendeteksi golongan darah menggunakan ESP32-CAM. Alat ini menggunakan kamera OV2640 untuk menangkap citra, yang diproses menggunakan Tensorflow Object Detection API sebagai framework untuk melatih serta mengolah citra darah. Model latih akan digunakan pada kondisi pendeteksian langsung dan ditampilkan dalam bentuk jendela program golongan darah beserta tingkat akurasinya. Dalam penelitian ini pengujian dilakukan menggunakan 20 dataset dengan jarak pengukuran antara ESP32-CAM dengan citra golongan darah yaitu sejauh 20 cm. Hasil yang didapat selama pengujian mayoritas golongan darah yang dapat terdeteksi adalah golongan darah AB.Kata kunci: ESP32-CAM, Tensorflow, Python, Golongan Darah, Pengolahan Citra ABSTRACTBlood group detection is performed to determine the blood group. Currently, in detecting blood type, it still relies on the ability of the human eyeThis paper presents a human blood group detection device using ESP32-CAM. This tool uses ESP32-CAM to capture images, and the Tensorflow Object Detection API as a framework used to train and process an image. The way this tool works is that the ESP32-CAM will capture an image of the blood sample and then send it via the IP address. Through the IP Address, the python program will access the image, then the image will be processed based on a model that has been previously trained. The results of this processing will be displayed in the form of a window program along with the blood type and level of accuracy. In this study, testing was carried out based on the number of image samples, the number of datasets, and the measurement distance. The ideal measurement distance between the ESP32-CAM and the blood group image is 20 cm long. The results obtained during the testing of the majority of blood groups that can be detected are AB blood group.Keywords: ESP32-CAM, Tensorflow, Python, Blood Type, Image Processing


1978 ◽  
Vol 253 (2) ◽  
pp. 377-379 ◽  
Author(s):  
M. Nagai ◽  
V. Davè ◽  
B.E. Kaplan ◽  
A. Yoshida

Nature ◽  
1961 ◽  
Vol 191 (4784) ◽  
pp. 149-150 ◽  
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W. T. J. MORGAN

1988 ◽  
Vol 178 (1) ◽  
pp. 111-120 ◽  
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Hasse Karlsson ◽  
Karl-Anders Karlsson ◽  
Karin Nilson ◽  
Bo E. Samuelsson ◽  
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1992 ◽  
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pp. 10925-10929 ◽  
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I. Mouro ◽  
B. Cherif-Zahar ◽  
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Biochemistry ◽  
1973 ◽  
Vol 12 (10) ◽  
pp. 1955-1961 ◽  
Author(s):  
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Byron Anderson ◽  
Elvin A. Kabat ◽  
Flavio Gruezo ◽  
Jerry Liao

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