Applied Journal of Physical Science

The human brain is made up of several hundreds of billions of interconnected neurons that process information in parallel. Researchers in the field of artificial intelligence have successfully demonstrated a considerable level of intelligence on chips and this has been termed Neural Networks (NNs).  Neural networks, also known as artificial neural networks (ANNs) or simulated neural networks (SNNs), are a subset of machine learning (ML) and they are at the heart of deep learning algorithms. These subsets of ML have their names and structures derived from the human brain and the way that biological neurons signal to one another. A class of NNs that are often used in processing digital data images is the Convolutional Neural Network (CNN or ConvNet). The human brain processes a huge amount of information with each neuron having its own receptive field connected to other neurons in a way that they cover the entire visual field. Mimicking the biological technique, where the neurons only respond to stimuli in the restricted region of the visual field referred to as the receptive field, each neuron in the CNN processes data only in its receptive field. In this review paper, the architecture and application of CNN are presented. Its evolution, concepts, and approaches to solving problems related to digital images, computer vision and are also examined.