Convolutional neural network based SARS-CoV-2 patients detection model using CT images

2021 ◽  
Vol 9 (2) ◽  
pp. 211
Author(s):  
Rawad Hammad ◽  
Mohammed Redha Qader ◽  
Vikram Bali ◽  
Shahnawaz Khan ◽  
K. Thirunavukkarasu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Ct Images ◽  
Detection Model

Convolutional neural network based SARS-CoV-2 patients detection model using CT images

2021 ◽  
Vol 9 (2) ◽  
pp. 211
Author(s):  
Shahnawaz Khan ◽  
K. Thirunavukkarasu ◽  
Rawad Hammad ◽  
Vikram Bali ◽  
Mohammed Redha Qader
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Ct Images ◽  
Detection Model

Pancreatic cancer tumor analysis in CT images using patch-based multi-resolution convolutional neural network

2021 ◽  
Vol 68 ◽  
pp. 102652
Author(s):  
Vahid Asadpour ◽  
Rex A. Parker ◽  
Patrick R. Mayock ◽  
Samuel E. Sampson ◽  
Wansu Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Pancreatic Cancer ◽  
Convolutional Neural Network ◽  
Ct Images ◽  
Cancer Tumor

Region-growing fully convolutional neural network interactive segmentation of liver CT images

2021 ◽  
Vol 36 (9) ◽  
pp. 1294-1304
Author(s):  
Li-juan ZHANG ◽  
◽  
Run ZHANG ◽  
Dong-ming LI ◽  
Yang LI ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Region Growing ◽  
Ct Images ◽  
Interactive Segmentation ◽  
Liver Ct

scholarly journals A Detection algorithm based on Convolutional Neural Network

2018 ◽  
Author(s):  
Fei Rong ◽  
Li Shasha ◽  
Xu Qingzheng ◽  
Liu Kun
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Training Model ◽  
Test Sample ◽  
Training Sample ◽  
Detection Algorithm ◽  
Small Scale ◽  
Detection Model ◽  
Sample Data ◽  
Ratio Change

The Station logo is a way for a TV station to claim copyright, which can realize the analysis and understanding of the video by the identification of the station logo, so as to ensure that the broadcasted TV signal will not be illegally interfered. In this paper, we design a station logo detection method based on Convolutional Neural Network by the characteristics of the station, such as small scale-to-height ratio change and relatively fixed position. Firstly, in order to realize the preprocessing and feature extraction of the station data, the video samples are collected, filtered, framed, labeled and processed. Then, the training sample data and the test sample data are divided proportionally to train the station detection model. Finally, the sample is tested to evaluate the effect of the training model in practice. The simulation experiments prove its validity.

scholarly journals Lightweight Convolutional Neural Network Based Intrusion Detection System

2020 ◽  
pp. 808-817
Author(s):  
Vinh Pham ◽  
◽  
Eunil Seo ◽  
Tai-Myoung Chung
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Convolutional Neural Network ◽  
Network Traffic ◽  
Detection System ◽  
Image Data ◽  
Training Data ◽  
Deep Packet Inspection ◽  
Detection Model

Identifying threats contained within encrypted network traffic poses a great challenge to Intrusion Detection Systems (IDS). Because traditional approaches like deep packet inspection could not operate on encrypted network traffic, machine learning-based IDS is a promising solution. However, machine learning-based IDS requires enormous amounts of statistical data based on network traffic flow as input data and also demands high computing power for processing, but is slow in detecting intrusions. We propose a lightweight IDS that transforms raw network traffic into representation images. We begin by inspecting the characteristics of malicious network traffic of the CSE-CIC-IDS2018 dataset. We then adapt methods for effectively representing those characteristics into image data. A Convolutional Neural Network (CNN) based detection model is used to identify malicious traffic underlying within image data. To demonstrate the feasibility of the proposed lightweight IDS, we conduct three simulations on two datasets that contain encrypted traffic with current network attack scenarios. The experiment results show that our proposed IDS is capable of achieving 95% accuracy with a reasonable detection time while requiring relatively small size training data.

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