[PDF] Top 20 An Ensemble of Convolutional Neural Networks Using Wavelets for Image Classification
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An Ensemble of Convolutional Neural Networks Using Wavelets for Image Classification
... Like the CIFAR-10 results, when we compare our OR fusion method to other methods like MLP and ELM, ours prevails in classification accuracy. Our pro- posed method maximizes the strength of each subband network ... See full document
20
OCT Image Classification Model using Hybrid Filters and Convolutional Neural Networks
... The existing literature with regard to OCT imaging reveals that it is tedious to manually detect retinal diseases since the acquired images mostly may have low quality or speckles. There also arises a need of an ... See full document
6
Cancer Hallmark Text Classification Using Convolutional Neural Networks
... text classification using machine learning methods that emphasize feature learning rather than manual feature ...on convolutional neural networks ...for image processing (LeCun ... See full document
9
Autonomous and Real Time Rock Image Classification using Convolutional Neural Networks
... images using the traditional image classification pipeline of feature selection and then training a ...first using transfer learning from networks trained on the ImageNet ...The ... See full document
123
A Novel Method for Remotely Sensed Hyperspectral Image Classification Based on Convolutional Neural Network
... the neural network is retrained and such active batch selection will iterate until the budget (the upper limit of label queries) is reached (Licciardi et ...for neural network and tested with different ... See full document
10
Classification of lung nodules in CT scans using three-dimensional deep convolutional neural networks with a checkpoint ensemble method
... nodule classification method that uses three dimensional deep convolutional neural networks (DCNNs) and an ensemble method to distinguish nodules between ...deep convolutional ... See full document
10
Classification of lung sounds using convolutional neural networks
... device. Using this device, we recorded 17,930 lung sounds from 1630 ...the convolutional neural network (CNN). Since using MFCC features with a SVM algorithm is a generally accepted ... See full document
9
Comparative Study of Artificial Neural Networks and Convolutional Neural Network for Crop Disease Detection
... and classification in crops. The crop disease detection and classification will be done using image processing techniques of image segmentation, feature extraction, feature selection ... See full document
5
Unified Framework For Deep Learning Based Text Classification
... artificial neural networks, which are inspired by biological brain model made of ...are convolutional neural network (CNN), deep belief networks, recurrent neural networks ... See full document
5
Deep Learning Pre-Trained Architecture Of Alex Net And Googlenet For DICOM Image Classification
... query image and increase the classification ...medical image classification is the descriptiveness and discriminative power of features extraction are critical to attain good ... See full document
7
Convolutional Neural Networks for Sentence Classification
... for image clas- sification, feature extractors obtained from a pre- trained deep learning model perform well on a va- riety of tasks—including tasks that are very dif- ferent from the original task for which the ... See full document
6
ACM: Adaptive Cross-Modal Graph Convolutional Neural Networks for RGB-D Scene Recognition
... RGB image classification has achieved significant perfor- mance improvement with the resurge of deep convolutional neural ...RGB image still have several limitations when applied to ... See full document
9
Spam detection in im images using convolutional neural networks
... precisely what this paper intends tom illustrate. As mentioned earlier in this paper, classifying emails has been a standard classification problem for ears. But we are no longer limited to emails anymore. ... See full document
6
Evaluating the performance of convolutional neural networks with direct acyclic graph architectures in automatic segmentation of breast lesion in US images
... of convolutional neural networks and reduce the sensitivity to network initialization, a Batch Normalization layer is used between convolutional layers and nonlinearities, such as ReLU ... See full document
13
Cystoscopy Image Classification Using Deep Convolutional Neural Networks
... As explained in the proposed method, the method presented in this paper consists of three important steps, including the extraction of features from cystoscopy images and feature reduction, and adaptive learning rates. ... See full document
13
Brain Tumor Classification Using Convolutional Neural Networks
... various image techniques such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI)and ultrasound image are used to evaluate the tumor in a brain, lung, liver, breast, ...manual ... See full document
5
Ensemble feature learning for material recognition with convolutional neural networks
... named ensemble learning for material recognition with convolutional neural networks ...the ensemble features, which achieves higher recognition ...proposed ensemble feature ... See full document
11
Understanding Convolutional Neural Networks for Text Classification
... of Convolutional Neural Networks (CNNs) for processing ...into image space, but for discrete sequence in- puts CNNs remain a ...the networks process and classify ...by using ... See full document
10
CloudBridge Waste Segregator Automation using Machine Learning
... In “Classification of Trash for Recyclability Status” [5] they have explained about using convolutional neural networks (CNN) and support vector machines (SVM) for classifying waste ... See full document
5
An Automated System for Identification of Skeletal Maturity using Convolutional Neural Networks Based Mechanism
... [1]. Classification processes that are widely used these days- Greulich Pyle [7] and Tanner-Whitehouse ...X-Ray image of a child and predict the bone ... See full document
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