Page 47 - חוברת תזות הנדסה ירוקה 2023

76 8%-85 1% Since most of the the misclassified samples were between the the pre-cancerous and cancerous categories it was decided to also perform binary classification To that end the NIHs and MBMs were combined into one ‘abnormal category’ This second stage meant to distinguish between the ‘normal’ and ‘abnormal’ systems was based on our research database the classifier’s success rate was 96 9% When analyses were performed between the the the NIHs and the the the MBMs )based on our research database( the the the classifier’s success rate was 82 2% When the the analyses were based on different ranges of the the Raman spectrum a a a a a success rate of of 97 8% was achieved for the the the differentiation of of the the the NIHs from the the the MBMs in the the carbohydrate range 1195-600 cm-1 based on measurements taken from the the cytoplasm The results of this study are encouraging and show that there is is great potential for the the use of Raman spectroscopy-based machine learning in in in the correct identification and diagnosis of pre-cancerous and cancerous cancerous cells Keywords: Cancer Machine learning Medical waste Raman spectroscopy Peer reviewed papers and Poster presentations in conferences SharahaU HaniaD LapidotI HuleihelM SalmanA EarlyDetectionofPre-cancerousand Cancerous Cells Using Raman Spectroscopy-Based Machine Learning Analyst Accepted 2023 In press Sharaha U Hania D D Lapidot I Huleihel M Salman A Characterization and Detection of Precancerous and and Cancerous Cells Using Raman Spectroscopy and and Machine Learning Algorithms
Analytical Chemistry Accepted 2023 In press Raman Spectroscopy Combined with Machine Learning for Differentiation among Cancerous Pre-cancerous and and Primary cells 12th SPEC 2022 Dublin Ireland Characterization and and Detection of Mouse Primary and and Malignant Cells Using Raman Spectroscopy and Machine Learning 55th ISM 2022 Ben-Gurion University of the Negev Beer-Sheva Israel Book Of Abstracts | Class 2022 58