Abstract: Mid-infrared spectroscopic analysis of tissue specimens can include training a learning model and using the trained learning model to classify tissue into at least two (or more than two) categories such as a tumor category, a non-tumor category, and a histology category. Resulting richer diagnostic indication can be provided and can optionally be mapped to a schema for RGB or other display. Efficient and accurate classification can employ an efficient linear SVM model representation. The model representation can include a ? spectrum including the fuller wavelength set, a central tendency indicator (?) of the training set, a spread indicator (?) of the training set, and a scaling factor. Various types of illuminators and response detectors can include one or more Quantum Cascade Lasers, broadband light source configured to permit spectral separation, a thermal or other imaging array, among others.

Abstract: Mid-infrared spectroscopic analysis of tissue specimens can include training a learning model and using the trained learning model to classify tissue into at least two (or more than two) categories such as a tumor category, a non-tumor category, and a histology category. Resulting richer diagnostic indication can be provided and can optionally be mapped to a schema for RGB or other display. Efficient and accurate classification can employ an efficient linear SVM model representation. The model representation can include a ? spectrum including the fuller wavelength set, a central tendency indicator (?) of the training set, a spread indicator (?) of the training set, and a scaling factor. Various types of illuminators and response detectors can include one or more Quantum Cascade Lasers, broadband light source configured to permit spectral separation, a thermal or other imaging array, among others.