Where is the machine looking? Locating discriminative light-scattering features by class-activation mapping

Used class-activation maps (CAM) to quantify the discriminative importance of light-scattering angles for our trained neural networks.The accuracy of our previous neural network was improved significantly using batch normalization, max-pooling, and leaky-ReLU activation function.Azimuthally-averaged CAMs highlighted scattering angles with high discriminative importance for specific particle classes and configuration of Mueller-matrix data input.CAMs can be used to inform scientists where to place detectors for strongest classification profile depending on the type of particles and measurement configuration.