deep convolutional neural network (CNN)
Automatic diagnosis of imbalanced ophthalmic images using a cost-sensitive deep convolutional neural network
... Meanwhile, deep convolutional neural network (CNN) models have demon- strated extraordinary performance in medical image recognition tasks ...representative deep learning CNN ...
20
Face Authentication Using Efficient Deep Convolutional Neural Network
... efficient deep Convolutional Neural network (CNN) is proposed to be used for face ...using deep convolutional neural ...efficient deep learning model is proposed to ...
6
Garments Texture Design Class Identification Using Deep Convolutional Neural Network
... Garments Texture Design Class Identification Using Deep Convolutional Neural Network.. S.M.[r] ...
14
A Deep Convolutional Neural Network Based Lung Disorder Diagnosis
... using Deep CNN. Novel network architecture is designed to capture the low-level textural features of the lung ...The network consists of four convolutional layers, followed by one average ...
11
Recognition of Brahmi words by Using Deep Convolutional Neural Network
... To minimize the over-fitting issue, this study trained the model in a controlled environment using a momentum value of 0.1 and it assisted to obtain the optimum performance. This study increased the number of ...
9
Weighted Channel Dropout for Regularization of Deep Convolutional Neural Network
... Another inspiration of this work comes from the ob- servation that in the stack of convolutional layers within CNN, all the channels generated by the previous layer are treated equally for the next layer. This is ...
8
Automatic Plastic Waste Segregation And Sorting Using Deep Learning Model
... weakly-supervised deep learning approach (DCNN-GPC) for detection and recognition of nuclear waste ...on deep learning and also able to detect and categorize unknown waste ...of Deep ...
5
Non Linear Text Regression with a Deep Convolutional Neural Network
... The results in Table 2 show that the neural net- work performs very well, with around 40% im- provement over the previous best results (Joshi et al., 2010). Our dataset splits are identical, and we have accurately ...
6
Automated Detection of Gender from Face Images
... known as fine grained ethnicity. According to earlier results, the prediction of the fine-grained ethnicity of an individual is the most challenging task, followed by age and lastly gender. A. Dehghan et al. [7] ...
5
A CNN Based Approach for Garments Texture Design Classification
... well-known deep Convolutional Neural Network (CNN) models AlexNet and VGG_S in two different garment datasets for the purpose of training and ...
7
Disease Detection in the Leaves of Multiple Plants
... The deep algorithms can be made useful in plant disease ...a deep learning based method for the detection of diseases effected in the leaves of ...the deep Convolutional Neural ...
5
Attributed Graph Classification via Deep Graph Convolutional Neural Networks
... In deep learning, Graph Convolutional Neural Networks (GCNs) are a class of neural networks, explicitly designed for in-depth analysis of graph-structured data (Bruna et ...of neural ...
124
Prediction of Wave Power Generation Using a Convolutional Neural Network with Multiple Inputs
... However, deep learning, which is derives from ML is able to learn high-level abstractions in data by utilizing hierarchical architectures ...the deep learning algorithms, CNN method has been considered one ...
18
Fine Grained Classification of Product Images Based on Convolutional Neural Networks
... a deep convolutional neural network that the convolution kernel size and the order of network connection are based on the high efficiency of the filter capacity and cover- ...of ...
19
Deep Learning Based Visual Tracking: A Review
... Da Zhang, Hamid Maei, Xin Wang, and Yuan-Fang Wang presented the first neural-network tracker that combines convolutional and recurrent networks with RL algorithm in [12]. The tracker is capable of ...
5
Three-dimensional convolutional restricted Boltzmann machine for human behavior recognition from RGB-D video
... three-dimensional convolutional restricted Boltzmann machine (3DCRBM) is proposed which can extract features from the raw RGB-D ...3D convolutional kernels can be applied to these four frames to extract ...
11
Facial Keypoints Detection with Deep Learning
... with deep convolutional neural ...deeper convolutional neural network such as the ResNet [6], and explore more image augmentation technique such as rotation and ...
8
MLVCNN: Multi-Loop-View Convolutional Neural Network for 3D Shape Retrieval
... descriptors, deep learning models and ensemble ...In deep learning models, Multi-View CNN (MVCNN) (Su et ...and Deep Local feature Aggregation Network(DLAN) (Fu- ruya and Ohbuchi 2016) are ...
8
Deep Convolution Neural Networks for Automatic Eyeglasses Removal
... of deep convolutional neural networks (DCNN) on super resolution, in this paper, a method based on deep convolutional neural network is developed for automatic eyeglasses ...
8
A comparison between two semantic deep learning frameworks for the autosomal dominant polycystic kidney disease segmentation based on magnetic resonance images
... on Deep Learning architectures using Convolutional Neural Networks (CNN) for the semantic segmentation of images, without needing to extract any hand-crafted ...the network remains the most ...
12