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Optimized U-Net Segmentation and Hybrid Res-Net for Brain Tumor MRI Images Classification

R. Rajaragavi, S. Palanivel Rajan
2022 Intelligent Automation and Soft Computing  
A brain tumor is a portion of uneven cells, need to be detected earlier for treatment. Magnetic Resonance Imaging (MRI) is a routinely utilized procedure to take brain tumor images.  ...  Here, the squirrel optimizer is utilized to tune the hyperparameters of the U-net model.  ...  We also combine bidirectional and attention modules to the U net model to extract more specific features. The hybridization of ResNet and Inception net was used to classify the tumor type.  ... 
doi:10.32604/iasc.2022.021206 fatcat:nre4zecqjjfdneqwmpvyscs5mq

Segmentation of Concrete Cracks by Using Fractal Dimension and UHK-Net

Qing An, Xijiang Chen, Haojun Wang, Huamei Yang, Yuanjun Yang, Wei Huang, Lei Wang
2022 Fractal and Fractional  
In this paper, we propose a novel approach by fusing fractal dimension and UHK-Net deep learning network to conduct the semantic recognition of concrete cracks.  ...  Then, we use the U-Net Haar-like (UHK-Net) network to construct the crack segmentation network.  ...  The CNN architecture includes thirteen convolution layers and a new classifier.  ... 
doi:10.3390/fractalfract6020095 fatcat:knarschbdrdxzbvbxfqsssmo4u

Error Recovery in Production Systems: A Petri Net Based Intelligent System Approach [chapter]

Nicholas G.
2008 Petri Net, Theory and Applications  
Hybrid nets such as the Petri -Neural Net are of particular interest.  ...  Ma (2000) investigated a neural network model for preliminary diagnostics using an input-output technique for shallow knowledge. A Petri Net embedded in a neural network was used to classify errors.  ...  For example, in (9b), as soon as the transition t f fires, the transition t f and the arc I (p 2 , t f ) are removed from the net.  ... 
doi:10.5772/5323 fatcat:hm7ottkc3fd2ra5osiekpgr7yu

GM-Net: Learning Features with More Efficiency [article]

Yujia Chen, Ce Li
2017 arXiv   pre-print
In this paper, we propose a series of Basic Units (BUs) and a two-level merging strategy to construct deep CNNs, referred to as a joint Grouped Merging Net (GM-Net), which can produce joint grouped and  ...  Deep Convolutional Neural Networks (CNNs) are capable of learning unprecedentedly effective features from images.  ...  Our baseline model contains only 0.7M parameters, but performs almost the same as Fractal Net [15] which has 40 times more parameters.  ... 
arXiv:1706.06792v1 fatcat:ueorcdwugnekxiuilbhgll42t4

SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates [article]

Lingkai Kong, Jimeng Sun, Chao Zhang
2020 arXiv   pre-print
Based on this perspective, we propose a neural stochastic differential equation model (SDE-Net) which consists of (1) a drift net that controls the system to fit the predictive function; and (2) a diffusion  ...  The Bayesian framework provides a principled way of uncertainty estimation but is often not scalable to modern deep neural nets (DNNs) that have a large number of parameters.  ...  As neural nets map an input x to an output y through a sequence of hidden layers, the hidden representations can be viewed as the states of a dynamical system.  ... 
arXiv:2008.10546v1 fatcat:2tffhdk3ovaoxf4ybbx42pjykq

Deep Learning Feature Extraction using Pre-Trained Alex Net Model for Indian Sign Language Recognition

2019 International journal of recent technology and engineering  
The real time classification of different signs is a challenging task due to the variation in shape and position of hands as well as due to the variation in the background which varies from person to person  ...  Multiple SVM (Support Vector Machine) is applied to classify Indian sign language in real time surroundings.  ...  Various models of deep convolution neural network exists namely LeNet, Alexnet, VGGNet, GoogLeNet, Residual Networks, DenseNet and Fractal Net.  ... 
doi:10.35940/ijrte.b2142.078219 fatcat:a4xwnr22vfedvabpuujgs4fjie

COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet

Adnan Saood, Iyad Hatem
2021 BMC Medical Imaging  
We propose computer-based techniques that prove to be reliable as detectors for infected tissue in lung CT scans.  ...  Results The results show the superior ability of in classifying infected/non-infected tissues compared to the other methods (with 0.95 mean accuracy), while the shows better results as a multi-class segmentor  ...  As a binary segmentor, Inf-Net focuses on edge information and allocates a portion of computations to highlight it.  ... 
doi:10.1186/s12880-020-00529-5 pmid:33557772 fatcat:ancwpszzqzfonjh6qraajapk7a

PF-Net: Point Fractal Network for 3D Point Cloud Completion

Zitian Huang, Yikuan Yu, Jiawen Xu, Feng Ni, Xinyi Le
2020 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
In this paper, we propose a Point Fractal Network (PF-Net), a novel learning-based approach for precise and highfidelity point cloud completion.  ...  To succeed at this task, PF-Net estimates the missing point cloud hierarchically by utilizing a feature-pointsbased multi-scale generating network.  ...  Concatenate them into a latent vector F. The size of F is 448.F will be passed through fully-connected layers[256,128,16,1]followed by Sigmoid-classifier to obtain predicted value.  ... 
doi:10.1109/cvpr42600.2020.00768 dblp:conf/cvpr/HuangYXNL20 fatcat:pzy7zbghyve7xoqng2zw72m3ei

Research of U-Net-Based CNN Architectures for Metal Surface Defect Detection

Ihor Konovalenko, Pavlo Maruschak, Janette Brezinová, Olegas Prentkovskis, Jakub Brezina
2022 Machines  
Recognition accuracy was analyzed as affected by the optimizer during neural network training.  ...  The highest recognition accuracy was attained using the U-Net model with a ResNet152 backbone. The results obtained on the test dataset were and .  ...  It uses a 1×1 convolution as a dimension reduction module to reduce the computation. By reducing the computation bottleneck, the depth and width can be increased.  ... 
doi:10.3390/machines10050327 fatcat:a3ndj5munjaujjgo4tateovkbq

Multi-Scale Fusion U-Net for the Segmentation of Breast Lesions

Jingyao Li, Lianglun Cheng, Tingjian Xia, Haomin Ni, Jiao Li
2021 IEEE Access  
To solve these problems, we propose a deep learning architecture, named Multi-scale Fusion U-Net (MF U-Net), which extracts the texture features and edge features of the image.  ...  Moreover, there are some convolutional layers with different receptive fields in MDCM, which improves the network's ability to extract multi-scale features.  ...  Multi fractal and RBRR perform poorly, because the manual features they extract are unsuitable for the dataset. DPM performed well, achieving high Recall and DSC.  ... 
doi:10.1109/access.2021.3117578 fatcat:34zlsm37rrca3n2h5m7q62fawy

An Exploration into the Detection of COVID-19 from Chest X-ray Scans Using the xRGM-NET Convolutional Neural Network

Gabriel Ackall, Mohammed Elmzoudi, Richard Yuan, Cuixian Chen
2021 Technologies  
In this paper, we report on our model and methods that were developed as part of a STEM enrichment summer program for high school students.  ...  Using the Cohen COVID-19 X-ray Database and the NIH ChestX-ray8 Database, we trained and constructed the xRGM-NET convolutional neural network (CNN) to detect COVID-19 in X-ray scans of the lungs.  ...  The pre-processed and augmented images are then fed into the xRGM-NET convolutional neural network to classify subjects as either positive or negative  ... 
doi:10.3390/technologies9040098 fatcat:bk4mfg7sajfx5kowyratwpqeda

Super-resolution reconstruction of cytoskeleton image based on A-net deep learning network [article]

Qian Chen, Haoxin Bai, Bingchen Che, Tianyun Zhao, Ce Zhang, Kaige Wang, Jintao Bai, Wei Zhao
2021 arXiv   pre-print
Utilizing the DWDC algorithm to construct new datasets and taking advantage of A-net neural network's features (i.e., considerably fewer layers), we successfully removed the noise and flocculent structures  ...  We, therefore, conclude that the proposed algorithm that combines A-net neural network with the DWDC method is a suitable and universal approach for exacting structural details of biomolecules, cells and  ...  Accordingly, the revised U-net network is named as A-net in this paper. Fig. 4. A-net network architecture.  ... 
arXiv:2112.09574v1 fatcat:2ddrs26odbhkxeybnwcjqpemly

Authentication, privacy, security can exploit brainwave by biomarker

Jeffrey Jenkins, Charles Sweet, James Sweet, Steven Noel, Harold Szu, Harold H. Szu, Liyi Dai
2014 Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XII  
We seek to augment the current Common Access Control (CAC) card and Personal Identification Number (PIN) verification systems with an additional layer of classified access biometrics.  ...  Prior to login, the user is shown a series of images on a computer display. They have been primed to click their mouse when the image is presented.  ...  First we used the J-48 decision tree classifier to perform classification on the user label (trying to see if we could classify a user based on the data) and present the following graph of accuracy and  ... 
doi:10.1117/12.2051323 fatcat:7pv3mzkqerarthwpw72nuzs2uu

A Few-Shot U-Net Deep Learning Model for COVID-19 Infected Area Segmentation in CT Images

Athanasios Voulodimos, Eftychios Protopapadakis, Iason Katsamenis, Anastasios Doulamis, Nikolaos Doulamis
2021 Sensors  
In this paper, we explore the efficacy of few-shot learning in U-Net architectures, allowing for a dynamic fine-tuning of the network weights as new few samples are being fed into the U-Net.  ...  Recently, to overcome these difficulties, few-shot learning (FSL) has been introduced as a general concept of network model training using a very small amount of samples.  ...  For this reason, in this paper, we adopted a U-Net model as the basic structure for the classifier performing the COVID-19 segmentation of CT scans.  ... 
doi:10.3390/s21062215 pmid:33810066 fatcat:j6pi7g33tbcbxiylgra5gsv2ja

The Growing Canvas of Biological Development: Multiscale Pattern Generation on an Expanding Lattice of Gene Regulatory Nets [chapter]

René Doursat
2010 Unifying Themes in Complex Systems  
Abstracting from biology in the same spirit as neural networks or swarm optimization, I hope to be contributing to a novel engineering paradigm of system construction that could complement or replace omniscient  ...  Each cell contains a genetic regulatory network (GRN), modeled as a feedforward hierarchy of switches that can settle in various on/off expression states.  ...  In the same spirit as artificial neural networks or ant colony optimization, my goal is less a faithful reproduction of biological mechanisms than their abstraction and potential application to computational  ... 
doi:10.1007/978-3-540-85081-6_26 fatcat:lge4bh3d2rbajch43s44vohjsm
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