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

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 NIMG-35. DEEP CONVOLUTIONAL NEURAL NETWORKS PROVIDE RAPID DIAGNOSIS OF FRESH BRAIN TUMOR SPECIMENS IMAGED WITH STIMULATED RAMAN HISTOLOGY

2017 ◽  
Vol 19 (suppl_6) ◽  
pp. vi149-vi149
Author(s):  
Todd C Hollon ◽  
Balaji Pandian ◽  
Yashar Niknafs ◽  
Spencer Lewis ◽  
Sandra Camelo-Piragua ◽  
...  
Keyword(s):  
Neural Networks ◽  
Brain Tumor ◽  
Convolutional Neural Networks ◽  
Rapid Diagnosis ◽  
Deep Convolutional Neural Networks ◽  
Stimulated Raman

scholarly journals A Deep Learning Approach to Detect Pornography Videos in Educational Repositories

2020 ◽  
Author(s):  
Pedro V. A. de Freitas ◽  
Antonio J. G. Busson ◽  
Álan L. V. Guedes ◽  
Sérgio Colcher
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Detection System ◽  
Automated Detection ◽  
Learning Approach ◽  
Multimodal Approach ◽  
Deep Convolutional Neural Networks ◽  
High Level

A large number of videos are uploaded on educational platforms every minute. Those platforms are responsible for any sensitive media uploaded by their users. An automated detection system to identify pornographic content could assist human workers by pre-selecting suspicious videos. In this paper, we propose a multimodal approach to adult content detection. We use two Deep Convolutional Neural Networks to extract high-level features from both image and audio sources of a video. Then, we concatenate those features and evaluate the performance of classifiers on a set of mixed educational and pornographic videos. We achieve an F1-score of 95.67% on the educational and adult videos set and an F1-score of 94% on our test subset for the pornographic class.

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