A new deep learning approach: Differential convolutional neural network

Deep learning structures have achieved unprecedented success rates in many scientific research areas. The well-known deep structure, convolutional neural network, is commonly used in pattern recognition studies. Convolutional neural network structures are composed of a feature extractor consisting of convolution and pooling layers and a fully connected network used as a classifier. In the first study of the thesis, GCNN, a strong kernel-based classifier, was adapted to CNN structure to increase the classification performance. This adaptation led a relative performance increase up to 44.45%, 39.69% and 43.57% for precision, recall, and F1-score, respectively. Although this adaptation yielded a significant increase in performance, it was observed that the convolutional part was weak in terms of representation. Therefore, the idea of developing a convolution technique with higher learning performance has emerged. A novel convolution technique named as Differential Convolution which considers directional changes among a pixel and its neighbors is proposed. Deep structures applying Differential Convolution are named as Differential Convolutional Neural Networks. These structures made a relative performance boost up to 55.29%, 58.43%, 41.75% and 56.43% for accuracy, precision, recall, and F1-score, respectively. Key Words: Deep learning, convolutional neural network, convolution techniques, general regression neural network, image classification, pattern recognition, artificial intelligence, machine learning.