Towards a Machine-Learning-Assisted Dielectric Sensing Platform for Point-of-Care Wound Monitoring
Hamed Rahmani, Maani M. Archang, Babak Jamali, Mahdi Forghani, Aaron Ambrus, Deeban Ramalingam, Zhengyang Sun, Philip O. Scumpia, Hilary A. Coller, Aydin Babakhani
Abstract
In this letter, we present a machine-learning-based solution to classify wounds and normal skin based on a dielectric spectroscopy approach. Using a commercial network analyzer, we have measured the dielectric constant of normal skin and different types of wounds from multiple living mice across a frequency range from 10 MHz to 20 GHz. The acquired data across a wide frequency range is processed by a Data Dimensionality Reduction technique to extract the optimum frequency for wound dielectric spectroscopy. The results of our analysis reveal that different types of wounds can be distinguished by acquiring the dielectric constants in a frequency range from 1 to 2 GHz. This finding relaxes the large bandwidth requirements of dielectric spectroscopy sensors. By adopting supervised learning classification tools, we have demonstrated that various tissue types across different samples can be classified with an accuracy of near 100%.