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A CNN is a modified DNN that is particularly well equipped to handling image data. CNN usually contains not only fully connected layers but also convolutional layers and pooling layers, which make a difference. Image is a matrix of pixel values, which should be flattened to vectors before feeding into DNN as DNN takes a vector as input. However, spatial information might be lost in this process. The convolutional layer can take a matrix or tensor as input and is able to capture the spatial and temporal dependencies in an image.

In the convolutional layer, the weight matrix (kernel) scans over the input image to produce a feature matrix. This process is called convolution operation. The pooling layer operates similar to the convolutional layer and has two types: Max Pooling and Average Pooling. The Max Pooling layer returns the maximum value from the portion of the image covered by the kernel matrix. The Average Pooling layer returns the average of all values covered by the kernel matrix. The convolution and pooling process can be repeated by adding additional convolutional and pooling layers. Deep convolutional networks have been successfully trained and used in image classification problems.

Figure 2 Convolution operation with stride size .