  Image classification has been a popular task due to its feasibility in
real-world applications. Training neural networks by feeding them RGB images
has demonstrated success over it. Nevertheless, improving the classification
accuracy and computational efficiency of this process continues to present
challenges that researchers are actively addressing. A widely popular embraced
method to improve the classification performance of neural networks is to
incorporate data augmentations during the training process. Data augmentations
are simple transformations that create slightly modified versions of the
training data and can be very effective in training neural networks to mitigate
overfitting and improve their accuracy performance. In this study, we draw
inspiration from high-boost image filtering and propose an edge
enhancement-based method as means to enhance both accuracy and training speed
of neural networks. Specifically, our approach involves extracting high
frequency features, such as edges, from images within the available dataset and
fusing them with the original images, to generate new, enriched images. Our
comprehensive experiments, conducted on two distinct datasets CIFAR10 and
CALTECH101, and three different network architectures ResNet-18, LeNet-5 and
CNN-9 demonstrates the effectiveness of our proposed method.
