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 chapter, we have explored 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 (BC), etc. The codes of the proposed model are available on GitHub .