scholarly journals A Deep Neural Network Classifier for Decoding Human Brain Activity Based on Magnetoencephalography

2017 ◽  
Vol 23 (2) ◽  
Author(s):  
Abdullah Caliskan ◽  
Mehmet Emin Yuksel ◽  
Hasan Badem ◽  
Alper Basturk
Keyword(s):  
Neural Network ◽  
Human Brain ◽  
Deep Neural Network ◽  
Brain Activity ◽  
Neural Network Classifier

scholarly journals Deep image reconstruction from human brain activity

2017 ◽  
Author(s):  
Guohua Shen ◽  
Tomoyasu Horikawa ◽  
Kei Majima ◽  
Yukiyasu Kamitani
Keyword(s):  
Neural Network ◽  
Image Reconstruction ◽  
Human Brain ◽  
Visual Information ◽  
Deep Neural Network ◽  
Brain Activity ◽  
Natural Images ◽  
Perceptual Content ◽  
Natural Image ◽  
Reconstruction Method

AbstractMachine learning-based analysis of human functional magnetic resonance imaging (fMRI) patterns has enabled the visualization of perceptual content. However, it has been limited to the reconstruction with low-level image bases (Miyawaki et al., 2008; Wen et al., 2016) or to the matching to exemplars (Naselaris et al., 2009; Nishimoto et al., 2011). Recent work showed that visual cortical activity can be decoded (translated) into hierarchical features of a deep neural network (DNN) for the same input image, providing a way to make use of the information from hierarchical visual features (Horikawa & Kamitani, 2017). Here, we present a novel image reconstruction method, in which the pixel values of an image are optimized to make its DNN features similar to those decoded from human brain activity at multiple layers. We found that the generated images resembled the stimulus images (both natural images and artificial shapes) and the subjective visual content during imagery. While our model was solely trained with natural images, our method successfully generalized the reconstruction to artificial shapes, indicating that our model indeed ‘reconstructs’ or ‘generates’ images from brain activity, not simply matches to exemplars. A natural image prior introduced by another deep neural network effectively rendered semantically meaningful details to reconstructions by constraining reconstructed images to be similar to natural images. Furthermore, human judgment of reconstructions suggests the effectiveness of combining multiple DNN layers to enhance visual quality of generated images. The results suggest that hierarchical visual information in the brain can be effectively combined to reconstruct perceptual and subjective images.

scholarly journals Characterization of deep neural network features by decodability from human brain activity

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Tomoyasu Horikawa ◽  
Shuntaro C. Aoki ◽  
Mitsuaki Tsukamoto ◽  
Yukiyasu Kamitani
Keyword(s):  
Neural Network ◽  
Human Brain ◽  
Deep Neural Network ◽  
Brain Activity

Withdrawn: Automated Diagnosis of Glaucoma Using Deep Neural Network Classifier

2019 ◽  
Vol 14 ◽  
Author(s):  
M. Madhumalini ◽  
T. Meera Devi
Keyword(s):  
Neural Network ◽  
Signal Transduction ◽  
Deep Neural Network ◽  
Neural Network Classifier ◽  
Current Signal ◽  
Automated Diagnosis ◽  
Signal Transduction Therapy ◽  
Copyright Permission ◽  
Legal Right

The article has been withdrawn on the request of the authors and the editor of the journal Current Signal Transduction Therapy. Bentham Science apologizes to the readers of the journal for any inconvenience this may have caused. BENTHAM SCIENCE DISCLAIMER: It is a condition of publication that manuscripts submitted to this journal have not been published and will not be simultaneously submitted or published elsewhere. Furthermore, any data, illustration, structure or table that has been published elsewhere must be reported, and copyright permission for reproduction must be obtained. Plagiarism is strictly forbidden, and by submitting the article for publication the authors agree that the publishers have the legal right to take appropriate action against the authors, if plagiarism or fabricated information is discovered. By submitting a manuscript the authors agree that the copyright of their article is transferred to the publishers, if and when the article is accepted for publication.

scholarly journals SINC: a scale-invariant deep-neural-network classifier for bulk and single-cell RNA-seq data

2019 ◽  
Vol 36 (6) ◽  
pp. 1779-1784 ◽  
Author(s):  
Chuanqi Wang ◽  
Jun Li
Keyword(s):  
Neural Network ◽  
Single Cell ◽  
Count Data ◽  
Deep Neural Network ◽  
Sequencing Depth ◽  
Supplementary Information ◽  
Neural Network Classifier ◽  
Rna Seq ◽  
Scale Invariant ◽  
Downstream Analysis

Abstract Motivation Scaling by sequencing depth is usually the first step of analysis of bulk or single-cell RNA-seq data, but estimating sequencing depth accurately can be difficult, especially for single-cell data, risking the validity of downstream analysis. It is thus of interest to eliminate the use of sequencing depth and analyze the original count data directly. Results We call an analysis method ‘scale-invariant’ (SI) if it gives the same result under different estimates of sequencing depth and hence can use the original count data without scaling. For the problem of classifying samples into pre-specified classes, such as normal versus cancerous, we develop a deep-neural-network based SI classifier named scale-invariant deep neural-network classifier (SINC). On nine bulk and single-cell datasets, the classification accuracy of SINC is better than or competitive to the best of eight other classifiers. SINC is easier to use and more reliable on data where proper sequencing depth is hard to determine. Availability and implementation This source code of SINC is available at https://www.nd.edu/∼jli9/SINC.zip. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Deep Neural Networks for the Classification of Bank Marketing Data using Data Reduction Techniques

2019 ◽  
Vol 8 (3) ◽  
pp. 4373-4378
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Data Reduction ◽  
Deep Neural Network ◽  
Training Data ◽  
Neural Network Classifier ◽  
Data Set ◽  
Reduction Techniques ◽  
Marketing Data ◽  
Bank Marketing

The amount of data belonging to different domains are being stored rapidly in various repositories across the globe. Extracting useful information from the huge volumes of data is always difficult due to the dynamic nature of data being stored. Data Mining is a knowledge discovery process used to extract the hidden information from the data stored in various repositories, termed as warehouses in the form of patterns. One of the popular tasks of data mining is Classification, which deals with the process of distinguishing every instance of a data set into one of the predefined class labels. Banking system is one of the realworld domains, which collects huge number of client data on a daily basis. In this work, we have collected two variants of the bank marketing data set pertaining to a Portuguese financial institution consisting of 41188 and 45211 instances and performed classification on them using two data reduction techniques. Attribute subset selection has been performed on the first data set and the training data with the selected features are used in classification. Principal Component Analysis has been performed on the second data set and the training data with the extracted features are used in classification. A deep neural network classification algorithm based on Backpropagation has been developed to perform classification on both the data sets. Finally, comparisons are made on the performance of each deep neural network classifier with the four standard classifiers, namely Decision trees, Naïve Bayes, Support vector machines, and k-nearest neighbors. It has been found that the deep neural network classifier outperforms the existing classifiers in terms of accuracy

scholarly journals AN ENHANCED METHOD OF LIVER LESION DETECTION USING DEEP NEURAL NETWORK, WATERSHED TRANSFORM AND GAUSSIAN MIXTURE MODEL TECHNIQUES IN MR IMAGES

2021 ◽  
pp. 335-340
Author(s):  
A.BathshebaParimala ◽  
R.S.Shanmugasundaram
Keyword(s):  
Neural Network ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Deep Neural Network ◽  
Gaussian Mixture ◽  
Metastatic Carcinoma ◽  
Neural Network Classifier ◽  
Mr Images ◽  
Malignant Growth ◽  
Aided Diagnosis

Cancer of the liver is one of the leading causes of death all over the world. Physically recognising the malignancy tissue is a difficult and time-consuming task. In the future, a computer-aided diagnosis (CAD) will be used in dynamic movement to determine the precise position for care. As a result, the primary goal of this research is to use a robotized approach to precisely identify liver cancer. Methods: In this paper, we suggest a new approach called the watershed Gaussian based deep learning (WGDL) strategy for accurately portraying malignant growth sores in liver MRI images. This project used a total of 150 images to build the proposed model. The liver was first isolated using a marker-controlled watershed division scale, and the malignancy-induced injury was then divided using the Gaussian mixture model (GMM) algorithm. Different surface highlights were removed from the sectioned locale after tumour division. These jumbled highlights were fed into a deep neural network (DNN) classifier for a computerised classification of three types of liver cancer: haemangioma (HEM), hepatocellular carcinoma (HCC), and metastatic carcinoma (MET). The following are the outcomes: Using a Deep Neural Network classifier and an unimportant approval deficiency of 0.053 during the characterization period, we were able to achieve a grouping precision of 98.38 percent at 150 ages. The system in our proposed approach is suitable for testing with a large data set and can assist radiologists in detecting liver malignant growth using MR images. KEYWORDS: computer-aided diagnosis (CAD), watershed Gaussian based deep learning, Gaussian mixture model, hepatocellular carcinoma, metastatic carcinoma, Deep Neural Network classifier

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