  Large-scale training of Convolutional Neural Networks (CNN) is extremely
demanding in terms of computational resources. Also, for specific applications,
the standard use of transfer learning also tends to require far more resources
than what may be needed. This work examines the impact of using AM-FM
representations as input images for CNN classification applications. A
comparison was made between AM-FM components combinations and grayscale images
as inputs for reduced and complete networks. The results showed that only the
phase component produced significant predictions within a simple network.
Neither IA or gray scale image were able to induce any learning in the system.
Furthermore, the FM results were 7x faster during training and used 123x less
parameters compared to state-of-the-art MobileNetV2 architecture, while
maintaining comparable performance (AUC of 0.78 vs 0.79).
