Fuzzy-Inspired Nanobiosensing for Multi-Feature Soft Classification of Tumor

This article proposes a novel fuzzy-inspired nanobiosensing framework for soft classification of tumor by using multiple features of the tumor, where interrelationships between different features are accounted for. For illustration purpose, two features of breast cancer, namely, tumor tissue abnormality and tumor foci separation, are utilized to determine the disease state. The sensing performance is then evaluated through the fuzzy relation transfer analysis. This analysis enables an intuitive yet systematic way to characterize the variation of classification fuzziness occurred during the sensing process facilitated by nanoscale contrast agents. Subsequently, the mean absolute error of the pre- and post-contrast membership functions is employed to evaluate the sensing integrity. Finally, numerical results are presented to demonstrate the principles of the proposed framework.