Investigating the Robustness of Fuzzy Deep Learning on Noisy Medical Images

Summary

This research tests a deep neuro-fuzzy system (DNFS) for medical image classification under six noise types and adversarial attacks. The DNFS matched state-of-the-art models on original images across seven biomedical datasets and showed superior average accuracy on noisy data. It was also tested for susceptibility to adversarial perturbations, which the authors report exposes weaknesses in deep learning.

Topics

• Adversarial machine learning

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