Enhanced image classification using edge CNN (E-CNN)

Recently, deep learning has become a hot topic in wide fields, especially in the computer vision that proved its efficiency in processing images. However, it tends to overfit or consumes a long learning time in many platforms. The causes behind these issues return to the huge number of learning parameters and lack or incorrect training samples. In this work, two levels of deep convolutional neural network (DCNN) are proposed for classifying the images. The first one is enhancing the training images with removing unnecessary details, and the second one is detecting the edges of the processed images for further reduction of learning time in the DCNN. The proposed work is inspired by the human eye's way in recognizing an object, where a piece of object can be helpful in the recognition and not necessarily the whole object or full colors. The goal is to speed up the learning process of CNN based on the preprocessed training samples that are precise and lighter to work well in real-time applications. The obtained results proved to be more significant for real-time classification as it reduced the learning process by (94%) in Animals10 dataset with a validation accuracy of (99.2%) in accordance with the classical DCNNs.