Dr Michael Greenop

Michael is a research fellow investigating clinical spectroscopy, including the validation of machine learning for clinical applications, with involvement in projects investigating cervical, prostate, and colon cancers. His primary project aims to provide a technology to detect human papillomavirus in urine to provide a fast and affordable cervical cancer screening method for low and middle-income countries. Michael is the primary author of a book chapter and author of publications discussing machine learning for the classification of cancers using machine learning and vibrational spectroscopy.

Michael has presented Fourier transform infrared spectroscopy (FTIR) for cervical cancer screening EASTER project opening in Harare, Zimbabwe, for his current position collaborating with the WHO, International Agency for Research in Cancer (IARC). He completed his PhD at Lancaster University, investigating single human skin (HaCaT) cells using three-dimensional (volumetric) Raman mapping and machine learning. His doctorate, which he won funding for following his MSc dissertation FTIR mapping ductal carcinoma invasion into the tumour microenvironment, included developing the first in situ flow chamber to facilitate Raman mapping of living cells.