Cervical carcinoma is one of the most common gynecological malignancies in the world. Here we have measured the 2D light scattering patterns of two representative types of cervical cancer cell lineage cells (HeLa, H8) at six different defocusing distances. The light scattering patterns vary at different defocusing distances, where the longer the defocusing distance, the larger the pattern area is. The classification performance for cervical cancer cells at different defocusing distances is evaluated based on support vector machine (SVM) classification algorithm. Speckle features are extracted by histogram of oriented gradient (HOG). Under six defocusing distances, the difference between the highest and lowest accuracy is 5.09%. The study of defocusing effects on cell classification with 2D light scattering static cytometry may help for the development of high speed and high performance imaging flow cytometry, and the combination of flow cytometry and machine learning holds great promise for automating the early clinical diagnosis of cervical cancer and other diseases.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.