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| http://dx.doi.org/10.4103/jrpp.JRPP_18_12 | DOI Listing |
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A novel deep learning-based pipeline architecture for pulp stone detection on panoramic radiographs.
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Department of Software Engineering, Faculty of Engineering, Muğla Sıtkı Koçman University, Muğla, 4800, Turkey.
Objectives: Pulp stones are ectopic calcifications located in pulp tissue. The aim of this study is to introduce a novel method for detecting pulp stones on panoramic radiography images using a deep learning-based two-stage pipeline architecture.
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Hunan Provincial Key Laboratory of Geotechnical Engineering for Stability Control and Health Monitoring, Hunan University of Science and Technology, Xiangtan, 411201, People's Republic of China.
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Department of Electrical Engineering, College of Engineering, Taif University, Taif, 21944, Saudi Arabia.
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Department of Chemistry, Faculty of Science, Damanhour University, Damanhour, Egypt.
Herein, a novel spectrofluorometric sensor is proposed for the sensitive analysis of two nonfluorescent mucolytic drugs, namely, acetylcysteine (ACT) and carbocisteine (CST), utilizing the newly synthesized 2-[(2-hydroxyethyl)-(2,8,10-trimethylpyrido[2',3':3,4]pyrazolo[1,5-a]pyrimidin-4-yl)-amino]-ethanol as a fluorescence probe (Flu. Probe). This fluorophore exhibits fluorescence emission at 445 nm upon excitation at 275 nm.
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