… the type of network specific to a problem. In this work, we have done a thorough literature
survey of Convolutional Neural Networks which is the widely used framework of deep learning. …

… We developed a 9-layer deep convolutional neural network (CNN) to automatically identify
5 different categories of heartbeats in ECG signals. Our experiment was conducted in original …

… Convolutional neural networks (CNNs) have been applied to visual tasks since the late …
brought them to the forefront of a neural network renaissance that has seen rapid progression …

… a deep convolutional neural network to capture the characteristics of degradation. We note
directly applying existing deep neural networks … and a neural network architecture where a …

… using a deep modular CNN architecture consist ing of layers of convolution, activation,
and pooling. The output of the CNN is then fed into a deep, fully connected neural network (FCN), …

… , diagnose, and refine deep CNNs. Abstract— Deep convolutional neural networks (CNNs)
have … However, the development of high-quality deep models typically relies on a substantial …

… deep convolutional neural networks can be used to approximate ridge functions nicely, which
hints some advantages of these structured networks in … -connected neural network can be …

… Deep Convolutional Neural Network (CNN) is a special type of Neural Networks, which has
… The powerful learning ability of deep CNN is primarily due to the use of multiple feature …

… Our results show that a large, deep convolutional neural network is capable of achieving
recordbreaking results on a highly challenging dataset using purely supervised learning. It is …

… CONCLUSION In this paper, we proposed a deep convolutional network for inverse problems
with a focus on biomedical imaging. The proposed method, which we call the FBPConvNet…