FuzzyDCNN: Incorporating Fuzzy Integral Layers to Deep Convolutional Neural Networks for Image Segmentation

Lin, Qiao; Chen, Xin; Chen, Chao; Garibaldi, Jonathan M.

Abstract

Convolutional neural networks (CNNs) have achieved the state-of-the-art performance in many application areas, due to the capability of automatically extracting and aggregating spatial and channel-wise features from images. Most recent studies have concentrated on modifying convolutional kernel size to achieve multi-scale spatial information. In this paper, we introduce a novel fuzzy integral module to the CNNs for fusing the information across feature channels. The fuzzy integral is a mathematical aggregation operator and is widely used in decision level fusion. Herein, we utilize a special case of fuzzy integrals namely ordered weight averaging (OWA) to merge information at feature level. Three publicly available datasets were used to evaluate the proposed fuzzy CNN model for image segmentation. The results show that the proposed fuzzy module helps in reducing the baseline model parameters by 58.54% while producing higher segmentation accuracy (measured by Dice) than the baseline method and a similar method reported in the literature.

Citation

Lin, Q., Chen, X., Chen, C., & Garibaldi, J. M. (2021). FuzzyDCNN: Incorporating Fuzzy Integral Layers to Deep Convolutional Neural Networks for Image Segmentation. In 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). https://doi.org/10.1109/fuzz45933.2021.9494456

• Conference Name: IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2021)
• Conference Location: Luxembourg, Luxembourg
• Start Date: Jul 11, 2021
• End Date: Jul 14, 2021
• Acceptance Date: May 7, 2021
• Online Publication Date: Aug 5, 2021
• Publication Date: Aug 5, 2021
• Deposit Date: Dec 2, 2021
• Publisher: IEEE
• Series Title: IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
• Series ISSN: 1544-5615
• Book Title: 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
• ISBN: 9781665444088
• DOI: https://doi.org/10.1109/fuzz45933.2021.9494456
• Public URL: https://nottingham-repository.worktribe.com/output/6847301
• Publisher URL: https://ieeexplore.ieee.org/document/9494456