A Deep Learning Approach to Early Cancer Detection using Near-Infrared Laser Scattering Profiles

In the early stages of most cancers, before lesions are visible on a CT or MRI, changes begin to occur at the cellular level as nuclei elongate and mitochondria cluster unevenly. As these organelles are responsible for much (>40%) of the optical scattering which occurs in a cell, changes in cell morphology and structure can largely affect the resulting optical signature. Variations in the physical properties of different cancer types leads to a distinct scattering profile unique to each disease. In this study, optical scattering patterns were investigated from five different cancer cell lines, which were irradiated in vitro with a NIR (854 nm) diode laser. The resulting patterns were collected with a CMOS beam profiler and used to train a convolutional neural network. Differences in these profiles were subtle yet significant enough to allow successful classification by the neural network. After being trained with a set of augmented images from each cancer type, the network was able to distinguish cell lines with an accuracy of up to 98.5%. The accurate classification of these patterns at low concentrations could contribute to the early detection of cancerous cells in otherwise healthy tissue. Current methods will also be discussed such as semantics and instance segmentation.