Diffuse reflectance spectroscopy (DRS) has already been successfully used for tissue discrimination during colorectal cancer surgery. In clinical practice, however, tissue often consists of several layers. Therefore, a novel multi-output convolutional neural network (CNN) was designed to classify multiple layers of colorectal cancer tissue simultaneously. DRS data was acquired with an array of six fibers with different fiber distances to sample at multiple depths. After training a 2D CNN with the DRS data as input, the first, second, and third tissue layers could be classified with mean accuracies of 0.90, 0.71, and 0.62, respectively.
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