[PDF] Top 20 High accuracy detection for T-cells and B-cells using deep convolutional neural networks

High accuracy detection for T-cells and B-cells using deep convolutional neural networks

... as T-cells and B-cells) within small amounts of whole blood is a rather challenging ordeal due to the lack of techniques that enable the separation of leukocytes from a limited amount of whole ... See full document

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Plant Stress Detection Accuracy Using Deep Convolution Neural Networks

... then accuracy measured using mean absolute percentage ...the deep convolution neural network model developed, and analysis of the accuracy of the model done based on the SoftMax ... See full document

8

Captioning for Motion Detection for video surveillance Applications using Deep Learning

... model neural network which automatically learns to features from the images and generate the ...trained using COCO dataset where the dataset is split into test, train and validation ...A ... See full document

6

Fungus Detection using Convolutional Neural Networks

... fungus detection using convolutional neural networks in order to identify whether the given classes of fungus is normal or ...websites. Using this dataset, we are applying ... See full document

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Integrated Management System For Sugarcane Disease Using Deep Learning Techniques-A Review

... for deep learning in a top - down and bottom-up and the plant ...features using CNN. The CNN gives the 99.6% accuracy and of the drawbacks of this technique is that some class's misclassifications ... See full document

5

Brain Tumor Classification Using Convolutional Neural Networks

... tumor detection is proposed by using Convolutional Neural Networks (CNN) ...by using small ...97.5% accuracy with low complexity and compared with the all other state of ... See full document

5

An Improved CNN Structure Model for Image Classification Recognition

... of using deep learning for imaging classification ...and deep learning from the limitations of traditional machine learning, and gives a detail introduction to the advantages of typical deep ... See full document

8

Breast cancer detection using deep convolutional neural networks and support vector machines

... With reference to the literature, this manuscript presents a new CAD system to classify benign and malignant mass lesions from mammogram samples using deep learning based SVM. The main contribution is that ... See full document

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Deep Learning Techniques for Object Detection

... retaining high processing speed of one-stage networks in order to maintain accuracy while eliminating ...Object Detection pertaining to motion-blur and de-focus, target ambiguity in video ... See full document

8

Pneumonia Detection Using Convolutional Neural Networks

... Radiograph of chest is penetrated through X-rays where the soft tissues produces a dark color and hard tissues like bones produces a bright color [2]. Patients diagnosed with pneumonia shows the chest cavity signs of ... See full document

6

UAV based slope failure detection using deep learning convolutional neural networks

... different accuracy assessment metrics used in this study, using only optical data resulted in a larger number of FPs than using a combination of optical data and slope ...use high-resolution ... See full document

24

Resiliency in Deep Convolutional Neural Networks

... combined with dropout. Thus, we expect that it will work well when the network is subjected to faults because this resembles dropout. Furthermore, Rprop is faster than standard gradient descent back-propagation. This ... See full document

109

Denoising of Images using Deep Convolutional Neural Networks (DCNN)

... set. Using a localized image patch violates the independence assumption in stochastic online learning, but combining the gradient from six separate images yields a 6 × 6 × 6 cube that in practice is a sufficient ... See full document

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Cystoscopy Image Classification Using Deep Convolutional Neural Networks

... the neural network. It is worth noting that neural networks are an array of artificial neurons, and information available is introduced and processed with a mathematical ...of neural ... See full document

13

Disease Detection of Plants using Deep Learning and Convolutional Neural Networks

... developed using CNN(Convolutional Neural Networks) through the help of Deep Learning ...Through Deep learning the accuracy in detecting an object gets ... See full document

5

Face recognition with Bayesian convolutional networks for robust surveillance systems

... recognition accuracy of unconstrained fa- cial images [7] using different classification techniques such as support vector machine (SVM) [8], stochastic modeling [9], neural networks [10] and ... See full document

10

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. Spammers have ... See full document

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Detection of Sarcasm in Text Data using Deep Convolutional Neural Networks

... 2. Sentiment Analysis and Opinion Mining. One thing that every person wants know that ”What other people think” to help them in a decision making process. Many years before when we were at midst of World Wide Web, we ... See full document

10

Deep Convolutional Neural Networks Capabilities for Binary Classification of Polar Mesocyclones in Satellite Mosaics

... for the detection of MCs based on the use of deep convolutional neural networks (DCNNs).. As a 21.[r] ... See full document

25

Empirical Assessment of Transfer Learning Techniques for Surgical Tools Classification

... of deep learning can be attributed to a number of key factors such as generation of a lot of computer accessible data, benchmarked datasets (like ImageNet,), increased computing power due to the GPU’s (Graphical ... See full document

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