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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Multi-feature based Benchmark for Cervical Dysplasia Classification Evaluation.

Tao Xu¹, Han Zhang², Cheng Xin¹

¹Computer Science and Engineering Department, Lehigh University, Bethlehem, PA, USA.

Pattern Recognition

|June 13, 2017

Summary

This summary is machine-generated.

This study introduces a new cervical cancer image dataset with expert diagnoses to evaluate classification algorithms. Both hand-crafted and deep learning features show effectiveness for cervical disease detection.

Keywords:

Cervical cancer screening computer aided diagnosis convolutional neural network image classification local binary patterns pyramid histogram

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Area of Science:

Medical imaging
Oncology
Computer vision

Background:

Cervical cancer is a leading cause of cancer deaths in women globally, particularly in less developed regions.
Accurate and accessible diagnostic tools are crucial for early detection and treatment.

Purpose of the Study:

To introduce a novel, expert-annotated image dataset for evaluating cervical disease classification algorithms.
To compare the performance of traditional hand-crafted features with deep learning features for cervical image analysis.

Main Methods:

A large dataset of Cervigram® images was curated from the US National Cancer Institute.
Three pyramid features (PLAB, PHOG, PLBP) were extracted from images.
Convolutional Neural Network (CNN) features were also extracted and evaluated.

Main Results:

Both hand-crafted pyramid features and CNN-based deep features demonstrated effectiveness in classifying cervical diseases.
Extensive evaluations were performed using seven classic classifiers.

Conclusions:

The developed multi-feature dataset and evaluation framework can serve as a valuable baseline for future research in image-based cervical cancer detection.
The findings highlight the potential of both feature engineering and deep learning approaches for improving diagnostic accuracy.