  Nowadays, deep learning models are increasingly required to be both
interpretable and highly accurate. We present an approach that integrates
Kolmogorov-Arnold Network (KAN) classification heads and Fuzzy Pooling into
convolutional neural networks (CNNs). By utilizing the interpretability of KAN
and the uncertainty handling capabilities of fuzzy logic, the integration shows
potential for improved performance in image classification tasks. Our
comparative analysis demonstrates that the modified CNN architecture with KAN
and Fuzzy Pooling achieves comparable or higher accuracy than traditional
models. The findings highlight the effectiveness of combining fuzzy logic and
KAN to develop more interpretable and efficient deep learning models. Future
work will aim to expand this approach across larger datasets.
