Brain tumors classification using electrical bioimpedance spectroscopy based on a multi-scale feature extraction network with frequency band attention mechanism.

Abstract

Electrical bioimpedance (EBI) measurement provides insights into the biophysical properties of tissues, offering valuable information for tumor diagnosis and classification. Deep learning has demonstrated distinct advantages in analyzing complex biomedical data. However, their applications in the rapid diagnosis of brain tumors had not been fully explored. In this study, 52 brain tumor samples were collected for EBI measurement. A deep learning framework that integrates multi-scale (MS) impedance feature extraction with frequency band attention was developed for the analysis of bioimpedance spectra (1-349 kHz) and automatic tumor classification. The model used parallel convolutional kernels (sizes 1, 3, 5, 7, 9) to capture local and global features, alongside an attention module to prioritize diagnostic frequency bands. Model performance was evaluated using precision, sensitivity, specificity andF1-score. Significant differences in impedance values were observed among gliomas, meningiomas, and metastases. The proposed model exhibits high sensitivity and precision in tumor classification tasks, achievingF1-scores of 91.54% (gliomas vs meningiomas vs metastases), 99.61% (glioma vs metastasis), 93.12% (lower-grade gliomas vs glioblastomas), and 98.75% (1p/19q codeleted vs non-codeleted gliomas), with significant conductivity differences (p< 0.05) between tumor types. In summary, the proposed framework, which integrates MS features and adaptive frequency, improves the performance of EBI-based tumor classification, and shows promise as an accurate intraoperative tool for the rapid diagnosis of brain tumors.