A Review of Convolutional Neural Networks

By Arohan Ajit, K. Acharya, Abhishek Samanta. 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), 2020

683 citations recorded by the source corpus.

Before Convolutional Neural Networks gained popularity, computer recognition problems involved extracting features out of the data provided which was not adequately efficient or provided a high degree of accuracy. However in recent times, Convolutional Neural Networks have attempted to provide a higher level of efficiency and accuracy in all the fields in which it has been employed in most popular of which are Object Detection, Digit and Image Recognition. It employs a definitely algorithm of steps to follow including methods like Backpropagation, Convolutional Layers, Feature formation and Pooling. Also this article will also venture into use of various frameworks and tools that involve CNN model.

Subfields: Computer Vision and Pattern Recognition, Neurology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/55a616cfd598106d98a97eef61d0f8120e4211a4

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In Computer Vision and Pattern Recognition

In Neurology

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems (2020, background): “While still in their infancy, these techniques exhibit strong performance for applications such as computerized tomography [57, 193, 263], seismic processing [272], or various sparse data problems.”
Integrating Physics-Based Modeling with Machine Learning: A Survey (2020, background): “While still in their infancy, these techniques exhibit strong performance for applications such as computerized tomography [47, 174], seismic processing [253], or various sparse data problems.”
A Comprehensive Overview and Comparative Analysis on Deep Learning Models: CNN, RNN, LSTM, GRU (2023, background): “Moreover, the pooling layer enables CNNs to recognize objects even when their shapes are distorted or viewed from different angles, by incorporating various dimensions of an image through pooling [19].”
CNN-Based Projected Gradient Descent for Consistent CT Image Reconstruction (2017, background): “3) Learning-based Algorithms: Recently, there has been a surge in using deep learning to solve inverse problems in imaging [19]–[23], establishing new state-of-the-art results for tasks like sparse-view CT reconstruction [19].”