  Cytology image segmentation is quite challenging due to its complex cellular
structure and multiple overlapping regions. On the other hand, for supervised
machine learning techniques, we need a large amount of annotated data, which is
costly. In recent years, late fusion techniques have given some promising
performances in the field of image classification. In this paper, we have
explored a fuzzy-based late fusion techniques for cytology image segmentation.
This fusion rule integrates three traditional semantic segmentation models
UNet, SegNet, and PSPNet. The technique is applied on two cytology image
datasets, i.e., cervical cytology(HErlev) and breast cytology(JUCYT-v1) image
datasets. We have achieved maximum MeanIoU score 84.27% and 83.79% on the
HErlev dataset and JUCYT-v1 dataset after the proposed late fusion technique,
respectively which are better than that of the traditional fusion rules such as
average probability, geometric mean, Borda Count, etc. The codes of the
proposed model are available on GitHub.
