Abstract
Machine Learning Classification of Cervical Tissue Liquid Based Cytology Smear Images by Optomagnetic Imaging Spectroscopy
Highlights
Cervical cancer is the fourth most common cancer in women worldwide with 528,000 new cases and the second most common cancer in less developed regions (445,000 cases)
Cervical cytological samples were first screened with Optomagnetic Imaging spectroscopy and as a result, optomagnetic spectra were gathered for all considered cervical samples
Machine Learning (ML) algorithms are mainly used for image-based classification, either those images of single cervical cell or whole smear images of Papanicolaou smears
Summary
Cervical cancer is the fourth most common cancer in women worldwide with 528,000 new cases and the second most common cancer in less developed regions (445,000 cases). There were many attempts to develop automated screening systems that would lower the cost and improve the accuracy of existing screening tests for cervical cancer detection [5,6,7,8,9,10]. These systems are mainly based on automated inspection of the cytology samples and classification of cervical smear images into the healthy/abnormal group. Classification results for LBC samples, obtained by selected supervised learning algorithms have been compared, in order to investigate whether improved sample preparation method, i.e. LBC affects the accuracy of cervical cancer detection by OMIS
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