Investigating the Robustness of Fuzzy Deep Learning on Noisy Medical Images

This research studies how deep neuro-fuzzy systems (DNFS, a type of AI that combines deep learning with fuzzy logic, which handles uncertain or imprecise information) perform on medical images that contain noise (unwanted degradation that makes images unclear). The researchers tested the DNFS on seven different medical imaging datasets with six types of noise and adversarial attacks (deliberate perturbations designed to fool AI models), and found that the DNFS maintained better accuracy on noisy images compared to other state-of-the-art models, though both approaches remained vulnerable to adversarial attacks.