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This paper introduces the `Projectron' as a new neural network architecturethat uses Radon projections to both classify and represent medical images. Themotivation is to build shallow networks which are more interpretable in themedical imaging domain. Radon transform is an established technique that canreconstruct images from parallel projections. The Projectron first appliesglobal Radon transform to each image using equidistant angles and then feedsthese transformations for encoding to a single layer of neurons followed by alayer of suitable kernels to facilitate a linear separation of projections.Finally, the Projectron provides the output of the encoding as an input to twomore layers for final classification. We validate the Projectron on fivepublicly available datasets, a general dataset (namely MNIST) and four medicaldatasets (namely Emphysema, IDC, IRMA, and Pneumonia). The results areencouraging as we compared the Projectron's performance against MLPs with rawimages and Radon projections as inputs, respectively. Experiments clearlydemonstrate the potential of the proposed Projectron forrepresenting/classifying medical images.

Projectron -- A Shallow and Interpretable Network for Classifying Medical Images