Multi-feature based benchmark for cervical dysplasia classification evaluation

Tao Xu, Han Zhang, Cheng Xin, Edward Kim, L. Rodney Long, Zhiyun Xue, Sameer Antani, Xiaolei Huang

Research output: Contribution to journalArticlepeer-review

89 Scopus citations


Cervical cancer is one of the most common types of cancer in women worldwide. Most deaths due to the disease occur in less developed areas of the world. In this work, we introduce a new image dataset along with expert annotated diagnoses for evaluating image-based cervical disease classification algorithms. A large number of Cervigram® images are selected from a database provided by the US National Cancer Institute. For each image, we extract three complementary pyramid features: Pyramid histogram in L*A*B* color space (PLAB), Pyramid Histogram of Oriented Gradients (PHOG), and Pyramid histogram of Local Binary Patterns (PLBP). Other than hand-crafted pyramid features, we investigate the performance of convolutional neural network (CNN) features for cervical disease classification. Our experimental results demonstrate the effectiveness of both our hand-crafted and our deep features. We intend to release this multi-feature dataset and our extensive evaluations using seven classic classifiers can serve as the baseline.

Original languageEnglish (US)
Pages (from-to)468-475
Number of pages8
JournalPattern Recognition
StatePublished - Mar 1 2017

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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