Theoretical Understanding of Convolutional Neural Network: Concepts, Architectures, Applications, Future Directions

Convolutional neural networks (CNNs) are one of the main types of neural networks used for image recognition and classification. CNNs have several uses, some of which are object recognition, image processing, computer vision, and face recognition. Input for convolutional neural networks is provided through images. Convolutional neural networks are used to automatically learn a hierarchy of features that can then be utilized for classification, as opposed to manually creating features. In achieving this, a hierarchy of feature maps is constructed by iteratively convolving the input image with learned filters. Because of the hierarchical method, higher layers can learn more intricate features that are also distortion and translation invariant. The main goals of this study are to help academics understand where there are research gaps and to talk in-depth about CNN's building blocks, their roles, and other vital issues.

Automated summary

Convolutional neural networks (CNNs) are widely used in image recognition and classification, with applications ranging from object recognition to face recognition. CNNs learn a hierarchy of features from input images through a process of convolution, enabling them to extract complex features that are invariant to distortion and translation. This study aims to identify research gaps and provide detailed insights into the building blocks and important issues of CNNs.