Convolutional Neural Network, also known as CNN is a sub field of deep learning which is mostly used for analysis of visual imagery. CNN is a class of deep feedforward artificial neural networks (ANN). This Neural Network uses the already supplied dataset to it for training purposes, and predicts the possible future labels to be assigned. Any kind of data This Neural Network uses its strengths against the curse of dimensionality. A portion of the territories where CNNs are broadly utilized are image recognition, image classification, image captioning and object detection etc.

Factors affecting efficiency

• ♦ FiltersEach layer of the CNN is applied to the filters to be mapped onto the images for result declaration.
• ♦ Kernel SizeWe can calculate kernel size by the formula Kernel size
  
  n _inputs * n _outputs
  The kernel size of convolutional layer can be determined by
  k _w * k _h * c _in * c _out
  where c_out is the size of the bias.
• ♦ StrideStride is the number of pixels shifting over the information network. It is the distance to move, filter, and move faster with larger values. Stride can have different values but the most common one is stride = 1
• ♦ PaddingPadding has a value and that particular value is added to the input image on each end. Because the kernel scans the whole image to process it for computation, padding adds a little extra frame on the sides of the image to grant an extra room to the kernel.

CNN Architecture (5 Layers)

• ♦ Convolutional layerConvolutional layer is the backbone of any CNN working model. This layer is the one where pixel by pixel scanning takes place of the images and creates a feature map to define future classifications.
• ♦ Pooling layerPooling is also known as the down-sampling of the data by bringing the overall dimensions of the images. The information of each feature from each convolutional layer is limited down to only containing the most necessary data. The process of creating convolutional layers and applying pooling is continuous, may take several times.
• ♦ Fully connected input layerThis is also known as the flattening of the images. The outputs gained from the last layer are flattened into a single vector so that it can be used as the input data from the upcoming layer.
• ♦ Fully connected layerAfter the feature analysis has been done and it's time for computation, this layer assigns random weights to the inputs and predicts a suitable label.
• ♦ Fully connected Output layerThis is the final layer of the CNN model which contains the results of the labels determined for the classification and assigns a class to the images.

Consequently, the combination between CNN Algorithm & appropriate parameters will guide to make a decision on choosing Deep Learning for Image Classification.

Conclusion

• ♦ Cost and Time Benefits
• ♦ Quality and Accuracy In Results
• ♦ Store information on the entire network
• ♦ The ability to work with insufficient knowledge
• ♦ Good falt tolerance
• ♦ Distributed memory
• ♦ Gradual Corruption
• ♦ Ability to train machine
• ♦ The ability of parallel processing