Robust classification of low-grade cervical cytology following analysis with ATR-FTIR spectroscopy and subsequent application of self-learning classifier eClass |
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Authors: | Jemma G Kelly Plamen P Angelov Júlio Trevisan Anastasia Vlachopoulou Evangelos Paraskevaidis Pierre L Martin-Hirsch Francis L Martin |
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Institution: | (1) Centre for Biophotonics, Lancaster Environment Centre, Lancaster University, Lancaster, LA1 4YQ, UK;(2) Intelligent Systems Research Laboratory, School of Computing and Communications, Lancaster University, Lancaster, LA1 4WA, UK;(3) Lancashire Teaching Hospitals NHS Trust, Preston, UK;(4) Department of Obstetrics and Gynaecology, University Hospital of Ioannina, 45500 Ioannina, Greece |
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Abstract: | Although the UK cervical screening programme has reduced mortality associated with invasive disease, advancement from a high-throughput
predictive methodology that is cost-effective and robust could greatly support the current system. We combined analysis by
attenuated total reflection Fourier-transform infrared spectroscopy of cervical cytology with self-learning classifier eClass.
This predictive algorithm can cope with vast amounts of multidimensional data with variable characteristics. Using a characterised
dataset set A: consisting of UK cervical specimens designated as normal (n = 60), low-grade (n = 60) or high-grade (n = 60)] and one further dataset (set B) consisting of n = 30 low-grade samples, we set out to determine whether this approach could be robustly predictive. Variously extending the
training set consisting of set A with set B data produced good classification rates with three two-class cascade classifiers.
However, a single three-class classifier was equally efficient, producing a user-friendly, applicable methodology with improved
interpretability (i.e., better classification with only one set of fuzzy rules). As data from set B were added incrementally
to the training set, the model learned and evolved. Additionally, monitoring of results of the set B low-grade specimens (known
to be low-grade cervical cytology specimens) provided the opportunity to explore the possibility of distinguishing patients
likely to progress towards invasive disease. eClass exhibited a remarkably robust predictive power in a user-friendly fashion
(i.e., high throughput, ease of use) compared to other classifiers (k-nearest neighbours, support vector machines, artificial neural networks). Development of eClass to classify such datasets
for applications such as screening exhibits robustness in identifying a dichotomous marker of invasive disease progression. |
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