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Fast image scanning with deep max-pooling convolutional neural networks
2013
2013 IEEE International Conference on Image Processing
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Deep Neural Networks now excel at image classification, detection and segmentation. ...
We show how dynamic programming can speedup the process by orders of magnitude, even when max-pooling layers are present. ...
INTRODUCTION Deep Max-Pooling Convolutional Neural Networks are Deep Neural Networks (DNN) with convolutional and max-pooling layers. ...
doi:10.1109/icip.2013.6738831
dblp:conf/icip/GiustiCMGS13
fatcat:4xvlipf7wfatngc56qzv5vyiqy
Fast Image Scanning with Deep Max-Pooling Convolutional Neural Networks
[article]
2013
arXiv
pre-print
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Deep Neural Networks now excel at image classification, detection and segmentation. ...
We show how dynamic programming can speedup the process by orders of magnitude, even when max-pooling layers are present. ...
Introduction Deep Max-Pooling Convolutional Neural Networks are Deep Neural Networks (DNN) with convolutional and max-pooling layers. ...
arXiv:1302.1700v1
fatcat:ctcsougrtffxnij6i4mwrobmqu
AD or Non-AD: A Deep Learning Approach to Detect Advertisements from Magazines
2018
Entropy
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We employed convolutional neural networks to classify scanned images as either advertisements or non-advertisements (i.e., articles). ...
In this study, we analyzed visual features of images to detect advertising images from scanned images of various magazines. ...
Approach In this study, we employed convolutional neural networks to detect advertisements within scanned images from magazines. ...
doi:10.3390/e20120982
pmid:33266705
fatcat:urh3tdsyevdg5bkt2iplbjefpu
Sonar Image Target Detection and Recognition Based on Convolution Neural Network
2021
Mobile Information Systems
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In this paper, a convolution neural network is used to deal with the target task of sonar detection, and the performance of each neural network model in the sonar image detection and recognition task of ...
Recent advancements in deep learning offer an effective approach for the study in machine vision using optical images. ...
Convolutional Neural Network. A convolution neural network (CNN) is a kind of deep neural network [8] [9] [10] [11] [12] . ...
doi:10.1155/2021/5589154
fatcat:ybujo6frnjbhpa5xk2zbj24zey
PreNet: Parallel Recurrent Neural Networks for Image Classification
[chapter]
2017
Communications in Computer and Information Science
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Convolutional Neural Networks (CNNs) have made outstanding achievements in computer vision, e.g., image classification and object detection, by modelling the receptive field of visual cortex with convolution ...
of fast convolution implementation. ...
The most similar work to our PreNet is ReNet [35] which also replaces the convolution+pooling layer with four recurrent neural networks. ...
doi:10.1007/978-981-10-7302-1_38
fatcat:7trwryami5ba3pffq4kdd2uoly
BHCNet: Neural Network based Brain Hemorrhage Classification Using Head CT Scan
2021
IEEE Access
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The convolutional neural network extracts the useful feature with the filter size of 3x3 and max-pooling reduce the features for the net layers with 2x2 pooling size. ...
VGG has consisted of a large network of layers of about 16 and 19 layers with dramatic repetition of the block of large convolutional layers set and max-pooling layers. ...
doi:10.1109/access.2021.3102740
fatcat:coeu3t7wwreefomwl5ugl4drba
A Survey on Deep Learning Architectures and Frameworks for Cancer Detection in Medical Images Analysis
2020
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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The study includes some dominant deep learning algorithms such as convolution neural network, fully convolutional network, autoencoder, and deep belief network to analyze the medical image and to detect ...
Deep learning contributes to enhanced performance and better prediction in detection of cancer with medical images. ...
Enhanced image detection and classification using convolutional neural network (CNN) models applied on 3D lung CT scan images with few phases on the proposed work [19] . ...
doi:10.35940/ijitee.k7654.0991120
fatcat:4qkd3kdqvjgu7n2g7wnmnyiici
Classification of Pulmonary CT Images by Using Hybrid 3D-Deep Convolutional Neural Network Architecture
2019
Applied Sciences
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In this study, two Convolutional Neural Network (CNN)-based models were proposed as deep learning methods to diagnose lung cancer on lung CT images. ...
To investigate the performance of the two proposed models (Straight 3D-CNN with conventional softmax and hybrid 3D-CNN with Radial Basis Function (RBF)-based SVM), the altered models of two-well known ...
Acknowledgments: We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X Pascal GPU used for this research. ...
doi:10.3390/app9050940
fatcat:sotrwrsuwbe6fm6twzdo2hg4rq
Automated System for Identifying COVID-19 Infections in Computed Tomography Images Using Deep Learning Models
2022
Journal of Healthcare Engineering
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This study utilizes a convolutional neural network (CNN), stacked autoencoder, and deep neural network to develop a COVID-19 diagnostic system. ...
Deep learning is one of the techniques that has been proven to be prominent and reliable in several diagnostic domains involving medical imaging. ...
e Deep Neural Network (DNN). A DNN is a basic neural network with more hidden nodes. ...
doi:10.1155/2022/5329014
pmid:35368962
pmcid:PMC8968354
fatcat:25na3rrymrg43nuxb744on3dz4
Deep Learning Based Chest X-Ray Image as a Diagnostic Tool for COVID-19
2020
2020 International Conference on Smart Electronics and Communication (ICOSEC)
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Scientific advancement of Artificial Intelligence in deploying a deep learning based medical field is remaining powerful to handle a huge data with accurate and fast results in medical imaging to diagnose ...
In Recent findings, the images of Chest X ray and CT scan have shown salient features that illustrates the severity of corona virus in lungs. ...
Convolution Neural Network Architecture gradient in the signal received is enrouted back. ...
doi:10.1109/icosec49089.2020.9215257
fatcat:wktacsj45jbavdri22tn3zkatu
Prediction of COVID-19 Cases Using CNN with X-rays
2020
2020 5th International Conference on Computing, Communication and Security (ICCCS)
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For image classification we used GoogleNet which is one of the CNN architecture and is also named as InceptionV1. The positively classified images by our model indicate the presence of COVID-19. ...
The results obtained in COVID prediction using GoogleNet with a training accuracy of 99% and testing accuracy of 98.5% emphasize the use of Transfer Learning models in disease prediction. ...
A Convolutional Neural Network (CNN) a type of artificial neural network operates on the principle of convolution. It consists of an input layer, followed by multiple hidden convolution layers. ...
doi:10.1109/icccs49678.2020.9276753
fatcat:duwkaspak5bmxbx63l3gjkaqry
APPLICATION OF DEEPLEARNING TECHNIQUES FOR COVID-19 DIAGNOSIS AND TREATMENT
2021
IJARCCE
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A computed tomography scan (CT) is the fastest method to diagnose patients with covid-19 variants. ...
This study furnishes an elaborate response with various Deep Learning (DL) techniques of Artificial intelligence to combat the novel coronavirus. ...
Overlapping Pooling: Convolutional neural network conventionally pool outputs of nearby clusters of neurons with no overlapping. ...
doi:10.17148/ijarcce.2021.10904
fatcat:v5htoxeixbeopgysaa3w3bvywu
Designation of Thorax and Non-Thorax Regions for Lung Cancer Detection in CT Scan Images using Deep Learning
2020
International Journal of Electrical & Electronic Systems Research (IEESR)
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As initial stage we proposed a thorax and non-thorax slice detection for CT scan images using deep convolutional neural network (DCNN) so that later it can be used to simplify the process of lung cancer ...
It comprises the following steps which involves designed the convolution layer, activation function, max pooling, fully-connected layer and output size. ...
CONV: convolution, pool: Max Pooling FC: fully connected layer Convolution neural network architecture design for DCNN 2. ...
doi:10.24191/jeesr.v17i1.006
fatcat:7drfdmd655g3tmcye6aqs5yhga
Convolutional Neural Networks for Character-level Classification
2017
IEIE Transactions on Smart Processing and Computing
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In this work, we analyze character recognition performance using the current state-of-the-art deep-learning structures. ...
Optical character recognition (OCR) automatically recognizes text in an image. OCR is still a challenging problem in computer vision. ...
Input image size of 60x60 b. A convolution layer by 20 maps with 5x5 kernels c. A max pooling layer overlapping regions of size 3x3 d. A convolution layer by 50 maps with 5x5 kernels e. ...
doi:10.5573/ieiespc.2017.6.1.053
fatcat:ewuywwkmerfjrh4jd7dbtfrjvi
Left Or Right Hand Classification From Fingerprint Images Using A Deep Neural Network
2020
Computers Materials & Continua
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In this paper, we applied the Classic CNN (Convolutional Neural Network), AlexNet, Resnet50 (Residual Network), VGG-16, and YOLO (You Only Look Once) networks to this problem, these are deep learning architectures ...
In this paper, we designed a deep learning system using deep convolution network to categorize fingerprints as coming from either the left or right hand. ...
AlexNet is a deep convolutional neural network which can classify high-resolution images. ...
doi:10.32604/cmc.2020.09044
fatcat:2koudtpy7jgpnch4loeyk3f3mi
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