Comparison of deep convolutional neural networks and statistical methods in i̇mage classification

Artificial neural networks have been used for many problems in the field of machine learning. It was not emphasized much until the early 2000s, but it came to the fore again in the 2000s. With the development of the computer systems, it has moved to deep networks. It has a place in many fields from image processing to medical applications. It is used with many auxiliary functions especially in image processing. Artificial neural networks have been used to solve many problems in the field of machine learning. It is also included in the image concept through the extensions that emerge and are open to development. Especially regression analysis methods are widely used. This situation has been compared with the history of convolutional neural networks, their place in the image world, the expected image expectations methods and the application of convolutional neural networks and forcing methods. This operational intent is linked to which method may be more effective.