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A fuzzy rank-based ensemble of CNN models for MRI segmentation

Glioblastoma is the most common subtype of malignant tumors of the central nervous system. Segmentation of the brain tumor image is crucial for accelerating the diagnosis and treatment of a patient. In the paper, an advanced neural network ensemble based on a fuzzy ranking approach for tumor segmentation is presented using a combination of convolutional neural network (CNN) architectures, namely SegResNet, UNETR, and SwinUNETR. The proposed method uses fuzzy rank-based unification of deep learners by considering two nonlinear functions in decision-making, which helps to take into account the confidence in the predictions of the three base models. The proposed method is evaluated using the BRATS 2023 MRI dataset and outperforms the state-of-the-art methods, achieving an average Dice score of 0.885+/-0.134. The statistical significance of the differences between the models and the ensemble is confirmed based on the Wilcoxon signed rank test, and the p-value is below 0.005.

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