We utilize a CNN based regression model that takes a regular convolutional neural network which is succeeded by regression layer at the end of the network. The feature extraction layers contain convolution, RELU and pooling layers. Convolution neural networks (CNN) are well known in the field of image recognition. The underlying assumption is that the complex geometrical features of an image are an ensemble of smaller and simpler patterns. CNN uses small filters that extract local features and progressively reduce the size of the input variable with each layer. The number of layers in CNN increases with an increase in the size of the input image. Consider the input image X of size N × M (Figure ). Each convolution + RELU and pooling layer produce the feature maps of reduced dimension as compared to its input. Additionally, the pooling layers produce an output map that is invariant to small changes within pooling window. The model is trained using a regression loss function which is given by the following equation ( ):