Fuzzy Rank-based Late Fusion Technique for Cytology image Segmentation
This work addresses the challenge of segmenting complex cytology images for medical diagnostics, but it is incremental as it applies a novel fusion method to existing models.
The paper tackles cytology image segmentation by proposing a fuzzy rank-based late fusion technique that combines UNet, SegNet, and PSPNet models, achieving maximum MeanIoU scores of 84.27% on the HErlev dataset and 83.79% on the JUCYT-v1 dataset, outperforming traditional fusion rules.
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.