Theoretical Understanding of Convolutional Neural Network: Concepts, Architectures, Applications, Future Directions
By Mohammad Mustafa Taye. De Computis, 2023
603 citations recorded by the source corpus.
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.
Subfields: Computer Vision and Pattern Recognition, Neurology, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/642d62995002c983727d9e2a8da4d5f481d5f1c2
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In Computer Vision and Pattern Recognition
In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions (2023, methodology): “12 , 91 The application of neural network-based DL technology is widespread across many industries and areas of study, including healthcare, sentiment analysis, NLP, the id of images, BI, cybersecurity, and many more [45,52,53].”