A Novel Adaptive Fuzzy Deep Learning Approach for Histopathologic Cancer Detection.

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Abstract

We proposed a novel model that integrates the fuzzy theory and group equivariant convolutional neural network for histopathologic cancer detection. The proposed fuzzy group equivariant convolutional neural network consists of the convolutional network, a novel fuzzy global pooling layer, and a fully connected network. In the fuzzy global pooling layer, the generated feature maps are transferred into the fuzzy domain by two different fuzzification methods. One of the fuzzy feature maps exploits the uncertainty information of histopathologic images, and the other keeps the original information. Furthermore, the fuzzy feature maps are processed by using Min-max operations. The experiments verified that the proposed method could always find the maximum fuzzy entropy and exploit and present the uncertainty of histopathologic images well. The experiments using the benchmark dataset demonstrate that the proposed model becomes more accurate and outperforms the existing models including the benchmark models. Compared to the benchmark model with 89.8% of accuracy, 96.3% of AUC, and 0.260 of negative log-likelihood loss, the proposed model obtained 91.7% of accuracy, 97.2% of AUC, and 0.214 of negative log-likelihood loss.