scholarly journals Automated plankton image analysis using convolutional neural networks

2018 ◽  
Vol 16 (12) ◽  
pp. 814-827 ◽  
Author(s):  
Jessica Y. Luo ◽  
Jean-Olivier Irisson ◽  
Benjamin Graham ◽  
Cedric Guigand ◽  
Amin Sarafraz ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Convolutional Neural Networks

scholarly journals Image Analysis using Deep Learning Techniques

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1385-1388
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Learning Techniques ◽  
Massive Information ◽  
Comparative Examination ◽  
Hypothetical Structure ◽  
The Way

In late years, critical learning methodologies especially Convolutional Neural Networks have been utilized in different solicitations. CNN's have appeared to be a key capacity to ordinarily expel broad volumes of data from massive information. The uses of CNNs have inside and out ended up being useful especially in orchestrating ordinary pictures. Regardless, there have been essential obstacles in executing the CNNs in a restorative zone as a result of the nonattendance of genuine getting ready data. Consequently, general imaging benchmarks, for instance, Image Net have been conspicuously used in the restorative not too zone notwithstanding the way that they are perfect when appeared differently about the CNNs. In this paper, a comparative examination of LeNet, AlexNet, and GoogLeNet has been done. Starting there, the paper has proposed an improved hypothetical structure for requesting helpful life structures pictures using CNNs. In perspective on the proposed structure of the framework, the CNNs building are required to beat the previous three plans in requesting remedial pictures.

scholarly journals A Survey on Techniques used in Medical Imaging Processing

2021 ◽  
Vol 2089 (1) ◽  
pp. 012013
Author(s):  
Priyadarshini Chatterjee ◽  
Dutta Sushama Rani
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Analysis ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Network Architecture ◽  
Medical Image Analysis ◽  
Artificial Neural ◽  
Automated Screening ◽  
To Come

Abstract Automated diagnosis of diseases in the recent years have gain lots of advantages and potential. Specially automated screening of cancers has helped the clinicians over the time. Sometimes it is seen that the diagnosis of the clinicians is biased but automated detection can help them to come to a proper conclusion. Automated screening is implemented using either artificial inter connected system or convolutional inter connected system. As Artificial neural network is slow in computation, so Convolutional Neural Network has achieved lots of importance in the recent years. It is also seen that Convolutional Neural Network architecture requires a smaller number of datasets. This also provides them an edge over Artificial Neural Networks. Convolutional Neural Networks is used for both segmentation and classification. Image dissection is one of the important steps in the model used for any kind of image analysis. This paper surveys various such Convolutional Neural Networks that are used for medical image analysis.

scholarly journals Medical Image Analysis using Convolutional Neural Networks: A Review

2018 ◽  
Vol 42 (11) ◽  
Author(s):  
Syed Muhammad Anwar ◽  
Muhammad Majid ◽  
Adnan Qayyum ◽  
Muhammad Awais ◽  
Majdi Alnowami ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Convolutional Neural Networks ◽  
Medical Image ◽  
Medical Image Analysis

scholarly journals Human Fatigue Evaluation by Face's Image Analysis Based upon Convolutional Neural Networks

2021 ◽  
Vol 24 (4) ◽  
pp. 582-603
Author(s):  
Azat Ilgizovich Bairamov ◽  
Timur Ruslanovich Faskhutdinov ◽  
Denis Marselevich Timergalin ◽  
Rustem Raficovich Yamikov ◽  
Vitaly Rudolfovich Murtazin ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Convolutional Neural Networks ◽  
Recognition Problem ◽  
Human Fatigue ◽  
Fatigue Evaluation

This article presents solutions to the person's fatigue recognition problem by the face's image analysis based on convolutional neural networks. In the present paper, existing algorithms were considered. A new model's architecture was proposed and implemented. Resultant metrics of the model were evaluated.

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