A GPU-based convolutional neural network approach for image classification

Deep learning obtains successful results in solving many machine learning problems. In this study, image classification process is performed by using Convolutional Neural Network (CNN) which is the most used architecture of deep learning. Image classification is used in a lot of basic field like medicine, education and security. Conditions that correct classification has vital importance may be especially in medicine field. Therefore, improved methods are needed in this issue. Although several algorithms for image classification have been developed over the years, they have not been used with the discovery of Convolutional Neural Networks. Convolutional Neural Networks provide better results than existing methods in the literature due to advantages such as processing by extracting hidden features, allowing parallel processing thanks to parallel structure, and real time operation. Furthermore, we use Convolutional Neural Networks in the proposed method. In this study, the image classification process is performed by using like a LeNet network model. The caffe library, which is often used for deep learning, is utilized. Our method is trained and tested with images of cats and dogs taken from the kaggle dataset. 10.000 tagged data is used for training and 5.000 unlabeled data is used for testing. Owing to Convolutional Neural Networks allow parallel processing, GPU technology has been used. In our method is used GPU technology and classification is evaluated with acceptable accuracy rate and speed performance.