Intraoperative frozen pathology is critical when a breast tumor is not diagnosed before surgery. However, frozen tumor tissues always present various microscopic morphologies, leading to a high misdiagnose rate from frozen section examination. Thus, we aimed to identify breast tumors using bioimpedance spectroscopy (BIS), a technology that measures the tissues’ impedance. We collected and measured 976 specimens from breast patients during surgery, including 581 breast cancers, 190 benign tumors, and 205 normal mammary gland tissues. After measurement, Cole-Cole curves were generated by a bioimpedance analyzer and parameters R₀/R_∞, f_c, and α were calculated from the curve. The Cole-Cole curves showed a trend to differentiate mammary gland, benign tumors, and cancer. However, there were some curves overlapped with other groups, showing that it is not an ideal model. Subsequent univariate analysis of R₀/R_∞, f_c, and α showed significant differences between benign tumor and cancer. However, receiver operating characteristic (ROC) analysis indicated the diagnostic value of f_c and R₀/R_∞ were not superior to frozen sections (area under curve [AUC] = 0.836 and 0.849, respectively), and α was useless in diagnosis (AUC = 0.596). After further research, we found a scatter diagram that showed a synergistic effect of the R₀/R_∞ and f_c, in discriminating cancer from benign tumors. Thus, we used multivariate analysis, which revealed that these two parameters were independent predictors, to combine them. A simplified equation, RF^′ = 0.2f_c + 3.6R₀/R_∞, based on multivariate analysis was developed. The ROC curve for RF′ showed an AUC = 0.939, and the sensitivity and specificity were 82.62% and 95.79%, respectively. To match a clinical setting, the diagnostic criteria were set at 6.91 and 12.9 for negative and positive diagnosis, respectively. In conclusion, RF′ derived from BIS can discriminate benign tumor and cancers, and integrated criteria were developed for diagnosis.