Experiments with large ensembles for segmentation and classification of cervical cancer biopsy images

To classify cervical cells as normal or cancer, the histological image must be segmented. After segmentation mean nuclear volume can be used to distinguish between normal and cancer cells. Due to the rapid reproduction of cancer cells, they have higher mean nuclear volume than typical normal cells. We propose a large ensemble of segmentations which separate normal and cancer cases based on the single feature of mean nuclear volume. Four basic segmentors with different parameters generate the segmentations. The mean nuclear volume is extracted from the segmentations. The dataset used for this paper contained multiple images from 30 normal and 32 cancer patients. Hematoxylin and eosin (H&E) was used to stain archival tissue sections from the normal cervix and cervical cancers. Results show it is possible to predict class with greater than 84% accuracy.