A Convolutional Neural Network for Cancer Detection via Optical Scattering Classification

In the early stages of most cancers, 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 morphology and structure can significantly affect the resulting optical signature. Variations in the physical properties of different cancer types lead 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 near-infrared 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 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 distinguished cell lines with an accuracy of up to 98.5%. The accurate classification of these patterns at low concentrations could lead to the early detection of cancerous cells in otherwise healthy tissue.