[PDF] Top 20 A study on deep convolutional neural networks for computer vision applications
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A study on deep convolutional neural networks for computer vision applications
... layers on the second and third dense blocks do not seem to utilize layers from the transition blocks as much as the features from layers inside the dense ...variations on the DenseNet architecture, a ... See full document
123
Convolutional neural networks for cell detection and counting : a case study of human cell quantification in zebrafish xenografts using deep learning object detection techniques
... focused on developing computer programs that auto- matically improve with experience ...of study that gives computers the ability to learn without being explicitly programmed" ...numerous ... See full document
150
Virtual Guide Dog: An Application to Support Visually-Impaired People through Deep Convolutional Neural Networks
... of applications besides the application responsible for the original data col- ...relies on accu- rately recognizing the dog’s activity in order to provide correct feedback for the user, we leverage ... See full document
8
Deep neural networks for image quality: a comparison study for identification photos
... traditional computer vi- sion algorithms to evaluate compliance requirements, in order to determine which of these approaches is better suited for the image quality classification ...focus on each method’s ... See full document
99
A novel architecture to classify histopathology images using convolutional neural networks
... the study of tissue structure under the microscope to determine if the cells are normal or ...needed. Convolutional neural network (CNN), a particular type of deep learning architecture, ... See full document
17
Comparative Study of First Order Optimizers for Image Classification Using Convolutional Neural Networks on Histopathology Images
... performance on the image classifier, as measured by the AUC, improves by adding more ...The convolutional neural network (CNN) has been used as an image classification algorithm for nearly two ... See full document
17
Deep Neural Networks for Handwritten Chinese Character Recognition
... addresses on envelopes, information in bank checks, and several other tedious tasks that humans need to ...perform. Convolutional Neural Networks are a power machine learning method for ... See full document
6
ADNet : computer-aided diagnosis for Alzheimer's disease using whole-brain 3D convolutional neural network
... better networks meant simply to stack more layers. With this study, they found the degradation problem, where traditional models similar to VGG stopped improving performance after a certain number of ... See full document
58
Classification of mice hepatic granuloma microscopic images based on a deep convolutional neural network
... years, deep learning, especially convolutional neural networks (CNNs) [6], as an excellent machine learning method for image classification, has also been proposed to analyze images in digital ... See full document
19
Lesion Classification in Mammograms Using Convolutional Neural Networks and Transfer Learning
... two convolutional layers and a fully connected layer and DeCAF - a pre-trained model with ImageNet) obtaining AUC values of ...another study that combined deep convolutional networks, ... See full document
9
Rotated Filters and Learning Strategies in Convolutional Neural Networks for Mammographic Lesions Detection
... During the first decade of the 21st century, ConvNets were still far away from the mainstream position they occupy today in the computer vision community. Still, many important results were obtained by ... See full document
105
A computer vision system for recognizing plant species in the wild using convolutional neural networks
... The choice of loss layer depends on how a system will classify its input. In this case, we have a system with several independent classes (e.g. species) and we want the ground truth class to have the highest score ... See full document
81
Contributions in face detection with deep neural networks
... the Computer Vision ...Recently, Deep Learning approaches started to be applied for Computer Vision tasks with great ...different applications, including Face Detection. Even ... See full document
143
Understanding Regularization in Deep Neural Networks: a Metalearning Approach
... years, deep learning has become more and more useful, solving increasingly complicated applications with greater accuracy over time, particularly in computer vision and natural language ... See full document
79
Deep learning model combination and regularization using convolutional neural networks
... convolution neural network that uses average pooling is LeCun et al ...A study [SMB10, BPL10] that evaluates the best performance method, if the average or the maxpooling one, concluded that depending ... See full document
72
Retinal image quality assessment using deep convolutional neural networks
... predicated on this idea of learning from example. In Deep Learning, instead of teaching a computer a massive list rules to solve the problem, it is given a model with which it can evaluate examples ... See full document
182
Transfer learning with convolutional neural networks for diabetic retinopathy image classification
... the deep network from scratch requires a substantial amount of time and huge datasets (hundreds of thousands of ...make deep learning algorithms very challenging in the context of medical images where, ... See full document
24
Ant genera identification using an ensemble of convolutional neural networks
... precision on top-1 and over 90% of accuracy and average precision on top-3 and top-5, show- ing a really good performance in the average use of the ...performance on top-1 (40%) and a great ... See full document
14
Neural networks and its applications in multivariate calibration.
... NEURAL NETWORKS AND ITS APPLICATIONS IN MULTIVARIATE CALIBRATION. Neural Networks are a set of mathematical methods and computer programs designed to simulate the information ... See full document
10
3D face recognition with descriptor images and shallow convolutional neural networks
... based on 2D face recognition perform very poorly in certain scenarios when the input images present variations in pose, illumination and facial ...of deep con- volutional neural networks is ... See full document
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