Abnormality Detection and Severity Classification of Cells based on Features Extracted From Papanicolaou Smear Images using Machine Learning

被引:4
作者
Ahhinaav, R. [1 ]
Brindha, D. [1 ]
机构
[1] PSG Coll Technol, Dept Biomed Engn, Coimbatore, Tamil Nadu, India
来源
2019 INTERNATIONAL CONFERENCE ON COMPUTER COMMUNICATION AND INFORMATICS (ICCCI - 2019) | 2019年
关键词
PAP Smear; Classification Algorithm; cervical cancer; supervised data analysis; Azure ML;
D O I
10.1109/iccci.2019.8822131
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
A Papanicolaou Smear (PAP) test is a screening method developed for cervical cancer that involves the microscopic examination of cervical cells carefully extracted and spread out as a smear and stained specially. A Pap test reveals premalignant and malignant changes and the changes that are due to non-carcinogenic conditions like inflammation. The diagnosis of this test are based upon key features of the nucleus and cytoplasm of the affected cell or the cell under observation. This work is aimed at devising a classification algorithm using supervised methods to efficiently classify the affected cells from normal cells and further group the affected cells Logistic Regression. ([9]) All algorithms and models were trained and validated using the Azure Machine Learning Studio.
引用
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页数:5
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