Fuzzy Knowledge Perception With Fault Tolerance and Brain-Inspired Reasoning for Diagnosis of Ultrasound Images

Fault tolerance to interference and interpretability are crucial for computer-aided diagnosis (CAD) systems. In clinical practice, deep learning models are often not accepted by doctors and patients due to their lack of diagnostic reasoning capabilities. Moreover, ultrasound imaging data contain significant noise and artifacts, making deep models vulnerable to interference, which frequently leads to misclassification. To address this issue, this study leverages fuzzy logic to achieve fault-tolerant perception of image content and employs a fuzzy trustworthy brain-inspired reasoning framework for diagnostic decision-making. In the fast-thinking module, we innovatively introduce the fuzzification of deep classification probabilities and construct a fault-tolerant perception set based on medical feature classification information. In the slow-thinking module, we establish a tensor decomposition-based knowledge graph, where medical feature information and membership degrees from the fault-tolerant perception set are jointly embedded to form a trustworthy factor matrix. The reasoning space reconstructed under this matrix guides the reasoning process from medical features toward the correct diagnostic outcome. To validate the effectiveness of the proposed model, we conducted diagnostic experiments on thyroid nodules and breast tumors, achieving AUC scores of 0.9549 and 0.9751, respectively. Experimental results demonstrate that our approach exhibits outstanding diagnostic performance on real-world ultrasound data.