Early tumor detection by multiple infrared unsupervised neural nets fusion

Abstract

The unsupervised classification algorithm called Lagrange constraint neural network (LCNN) has been successfully applied to the sub-pixel multispectral remote sensing, [25]. Here, we apply the LCNN to the early breast cancer detection using two-color mid and long infrared images of the breast. This could be a new paradigm shift that enabled smart neural network algorithm to sort out the underlying malignant heat sources for physician diagnoses. The nonintrusive 2-color passive infrared imaging that could be repeated for record track with no radiation hazard seems to be alternative paradigm shift for the first-line screening against breast cancer. The sub-pixel super-resolution capability of the remote sensing is equivalent to the sub-milimeter scaling of the close-up breast imaging for the vascular and the angiogenesis effects. We demonstrate the potential benefit of the multicolor mid & long infrared imaging capable for detecting the abnormal under-skin thermal textures as well as stage-zero detection of the ductal carcinoma in situ.