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1
Machine learning approach of automatic identification and counting of blood cells.
Alam, Mohammad Mahmudul;Islam, Mohammad Tariqul;
2019 healthcare technology letters DOI: 10.1049/htl.2018.509... 269 views
2
SITbench 1.0: A Novel Switch-Based Interaction Technique Benchmark.
Esiyok, Cagdas;Albayrak, Sahin;
2019 journal of healthcare engineering DOI: 10.1155/2019/5075163 337 views
3
Improvement of the Bonding Properties of Mineral Trioxide Aggregate by Elastin-Like Polypeptide Supplementation.
Kim, Hyun-Jung;Lee, Donghyun;Cho, Seungryong;Jang, Ji-Hyun;Kim, Sahng Gyoon;Kim, Sun-Young;
2019 scanning DOI: 10.1155/2019/3484396 312 views
4
Low level determination of thallium in biological and environmental reference materials by RNAA using several counting methods
J. Kucera,M. Vobecký,L. Soukal,D. Zakoucky,D. Vénos;J. Kucera;M. Vobecký;L. Soukal;D. Zakoucky;D. Vé...
1997 journal of radioanalytical and nuclear chemistry DOI: 10.1007/bf02055361 195 views

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image classification blood object detection learning (artificial intelligence) medical image processing automatic identification blood cells detection blood smear images cellular biophysics complete blood cell count machine learning approach neural nets patient diagnosis red blood cells white blood cells thallium environmental samples reference materials scintillation