… Convolutional neural networks (CNNs) … network renaissance that has seen rapid progression
since 2012. In this review, which focuses on the application of CNNs to image classification …

… classification [9]. In this work, we propose a customized CNN network for lung image patch
classification. … Convolutional networks for images, speech, and time series. The handbook of …

… image classification. We also study the composition of the different Convolutional neural
network on the result of image … deep convolutional networks for large-scale image recognition,” …

… Since artificial neural networks have shown a performance … scene classification, specially
convolutional neural networks (CNN)[4] [5] [6], this work focuses on identifying the best network …

… networks (ANNs) and the basic network layers of CNNs, (2) the classic predecessor network
models, (3) the recent SOAT network … of various image classification methods mentioned in …

… the development of CNN architecture in image classification. Then the advantages and
disadvantages of various convolution network architectures are compared and analyzed, and the …

… trend of convolution neural network model based on deep learning in image classification
are reviewed, which is mainly introduced from the aspects of typical network structure …

… the visualisation of image classification models, learnt using deep Convolutional Networks
(… the gradient of the class score with respect to the input image. The first one generates an …

… images … image classification. More specifically, the main contributions of this article are
threefold. 1) We systematically analyze CNNs and GCNs with a focus on HS image classification. …

… It can be thought that convolutional neural network (CNN) can automatically extract features
… learning convolutional network is used to achieve a high accuracy in classifying the images …