Deriving tumor detection models using convolutional neural networks from MRI of human brain scans

2020 ◽  
Vol 12 (2) ◽  
pp. 403-408
Author(s):  
T. Kalaiselvi ◽  
S. T. Padmapriya ◽  
P. Sriramakrishnan ◽  
Karuppanagounder Somasundaram
Keyword(s):  
Neural Networks ◽  
Human Brain ◽  
Convolutional Neural Networks ◽  
Tumor Detection ◽  
Brain Scans

Big data analysis for brain tumor detection: Deep convolutional neural networks

2018 ◽  
Vol 87 ◽  
pp. 290-297 ◽  
Author(s):  
Javeria Amin ◽  
Muhammad Sharif ◽  
Mussarat Yasmin ◽  
Steven Lawrence Fernandes
Keyword(s):  
Neural Networks ◽  
Big Data ◽  
Brain Tumor ◽  
Data Analysis ◽  
Convolutional Neural Networks ◽  
Tumor Detection ◽  
Big Data Analysis ◽  
Deep Convolutional Neural Networks

scholarly journals Dissociable Neural Representations of Adversarially Perturbed Images in Convolutional Neural Networks and the Human Brain

2021 ◽  
Vol 15 ◽  
Author(s):  
Chi Zhang ◽  
Xiao-Han Duan ◽  
Lin-Yuan Wang ◽  
Yong-Li Li ◽  
Bin Yan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Human Brain ◽  
Convolutional Neural Networks ◽  
Brain Activity ◽  
Neural Responses ◽  
Neural Representations ◽  
Artificial Neurons ◽  
Visual Areas ◽  
Two Systems ◽  
Visual Tasks

Despite the remarkable similarities between convolutional neural networks (CNN) and the human brain, CNNs still fall behind humans in many visual tasks, indicating that there still exist considerable differences between the two systems. Here, we leverage adversarial noise (AN) and adversarial interference (AI) images to quantify the consistency between neural representations and perceptual outcomes in the two systems. Humans can successfully recognize AI images as the same categories as their corresponding regular images but perceive AN images as meaningless noise. In contrast, CNNs can recognize AN images similar as corresponding regular images but classify AI images into wrong categories with surprisingly high confidence. We use functional magnetic resonance imaging to measure brain activity evoked by regular and adversarial images in the human brain, and compare it to the activity of artificial neurons in a prototypical CNN—AlexNet. In the human brain, we find that the representational similarity between regular and adversarial images largely echoes their perceptual similarity in all early visual areas. In AlexNet, however, the neural representations of adversarial images are inconsistent with network outputs in all intermediate processing layers, providing no neural foundations for the similarities at the perceptual level. Furthermore, we show that voxel-encoding models trained on regular images can successfully generalize to the neural responses to AI images but not AN images. These remarkable differences between the human brain and AlexNet in representation-perception association suggest that future CNNs should emulate both behavior and the internal neural presentations of the human brain.

scholarly journals Application Research of Deep Convolutional Neural Network in Computer Vision

2020 ◽  
Vol 2 (2) ◽  
pp. 23
Author(s):  
Lei Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Computer Vision ◽  
Face Recognition ◽  
Human Brain ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Convolution Neural Network ◽  
Data Set ◽  
Deep Convolution Neural Network

<p>As an important research achievement in the field of brain like computing, deep convolution neural network has been widely used in many fields such as computer vision, natural language processing, information retrieval, speech recognition, semantic understanding and so on. It has set off a wave of neural network research in industry and academia and promoted the development of artificial intelligence. At present, the deep convolution neural network mainly simulates the complex hierarchical cognitive laws of the human brain by increasing the number of layers of the network, using a larger training data set, and improving the network structure or training learning algorithm of the existing neural network, so as to narrow the gap with the visual system of the human brain and enable the machine to acquire the capability of "abstract concepts". Deep convolution neural network has achieved great success in many computer vision tasks such as image classification, target detection, face recognition, pedestrian recognition, etc. Firstly, this paper reviews the development history of convolutional neural networks. Then, the working principle of the deep convolution neural network is analyzed in detail. Then, this paper mainly introduces the representative achievements of convolution neural network from the following two aspects, and shows the improvement effect of various technical methods on image classification accuracy through examples. From the aspect of adding network layers, the structures of classical convolutional neural networks such as AlexNet, ZF-Net, VGG, GoogLeNet and ResNet are discussed and analyzed. From the aspect of increasing the size of data set, the difficulties of manually adding labeled samples and the effect of using data amplification technology on improving the performance of neural network are introduced. This paper focuses on the latest research progress of convolution neural network in image classification and face recognition. Finally, the problems and challenges to be solved in future brain-like intelligence research based on deep convolution neural network are proposed.</p>

scholarly journals Not so fast: Limited validity of deep convolutional neural networks as in silico models for human naturalistic face processing

2021 ◽  
Author(s):  
Guo Jiahui ◽  
Ma Feilong ◽  
Matteo Visconti di Oleggio Castello ◽  
Samuel A Nastase ◽  
James V Haxby ◽  
...  
Keyword(s):  
Neural Networks ◽  
Human Brain ◽  
Convolutional Neural Networks ◽  
Face Processing ◽  
In Silico ◽  
Processing System ◽  
Human Face ◽  
Deep Convolutional Neural Networks ◽  
Neural Representations ◽  
Fully Connected

Deep convolutional neural networks (DCNNs) trained for face identification can rival and even exceed human-level performance. The relationships between internal representations learned by DCNNs and those of the primate face processing system are not well understood, especially in naturalistic settings. We developed the largest naturalistic dynamic face stimulus set in human neuroimaging research (700+ naturalistic video clips of unfamiliar faces) and used representational similarity analysis to investigate how well the representations learned by high-performing DCNNs match human brain representations across the entire distributed face processing system. DCNN representational geometries were strikingly consistent across diverse architectures and captured meaningful variance among faces. Similarly, representational geometries throughout the human face network were highly consistent across subjects. Nonetheless, correlations between DCNN and neural representations were very weak overall—DCNNs captured 3% of variance in the neural representational geometries at best. Intermediate DCNN layers better matched visual and face-selective cortices than the final fully-connected layers. Behavioral ratings of face similarity were highly correlated with intermediate layers of DCNNs, but also failed to capture representational geometry in the human brain. Our results suggest that the correspondence between intermediate DCNN layers and neural representations of naturalistic human face processing is weak at best, and diverges even further in the later fully-connected layers. This poor correspondence can be attributed, at least in part, to the dynamic and cognitive information that plays an essential role in human face processing but is not modeled by DCNNs. These mismatches indicate that current DCNNs have limited validity as in silico models of dynamic, naturalistic face processing in humans.

P4-037: THE USE OF CONVOLUTIONAL NEURAL NETWORKS TO QUANTIFY AMYLOID PLAQUES IN POSTMORTEM HUMAN BRAIN

2006 ◽  
Vol 14 (7S_Part_27) ◽  
pp. P1446-P1446
Author(s):  
Kangway Chuang ◽  
Ziqi Tang ◽  
Michael Keiser ◽  
Laurel Beckett ◽  
Charlie S. DeCarli ◽  
...  
Keyword(s):  
Neural Networks ◽  
Human Brain ◽  
Convolutional Neural Networks ◽  
Amyloid Plaques ◽  
Postmortem Human Brain

scholarly journals Alzheimer’s Detection through 3D Convolutional Neural Networks

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Ryan Hogan ◽  
Christoforos Christoforou
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Three Dimensional ◽  
Multilayer Perceptrons ◽  
Brain Scans ◽  
Convolutional Networks ◽  
3D Network ◽  
Efficient Resource ◽  
Mri Scans

To inform a proper diagnosis and understanding of Alzheimer’s Disease (AD), deep learning has emerged as an alternate approach for detecting physical brain changes within magnetic resonance imaging (MRI). The advancement of deep learning within biomedical imaging, particularly in MRI scans, has proven to be an efficient resource for abnormality detection while utilizing convolutional neural networks (CNN) to perform feature mapping within multilayer perceptrons. In this study, we aim to test the feasibility of using three-dimensional convolutional neural networks to identify neurophysiological degeneration in the entire-brain scans that differentiate between AD patients and controls. In particular, we propose and train a 3D-CNN model to classify between MRI scans of cognitively-healthy individuals and AD patients. We validate our proposed model on a large dataset composed of more than seven hundred MRI scans (half AD). Our results show a validation accuracy of 79% which is at par with the current state-of-the-art. The benefits of our proposed 3D network are that it can assist in the exploration and detection of AD by mapping the complex heterogeneity of the brain, particularly in the limbic system and temporal lobe. The goal of this research is to measure the efficacy and predictability of 3D convolutional networks in detecting the progression of neurodegeneration within MRI brain scans of HC and AD patients.

Development of automatic glioma brain tumor detection system using deep convolutional neural networks

2020 ◽  
Vol 30 (4) ◽  
pp. 926-938 ◽  
Author(s):  
Thiruvenkadam Kalaiselvi ◽  
Thiyagarajan Padmapriya ◽  
Padmanaban Sriramakrishnan ◽  
Venugopal Priyadharshini
Keyword(s):  
Neural Networks ◽  
Brain Tumor ◽  
Convolutional Neural Networks ◽  
Detection System ◽  
Tumor Detection ◽  
Deep Convolutional Neural Networks
Sign in / Sign up

Export Citation Format

Share Document