ICSES Transactions on Image Processing and Pattern Recognition
Vol. 5, No. 1, Apr. 2019
Future Trends in Utilization of Deep Learning Techniques for Disease Recognition and Classification | Miscellaneous
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Miscellaneous |
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a Maharaja Agrasen Institute of Technology (MAIT), Delhi, India b Lovely Professional University, Phagwara, India
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This article has been retracted by International Computer Science and Engineering Society (ICSES) because of ethical misconduct, scientific distortion, or administrative error, and cannot be downloaded and used for any purpose based on the violation in ICSES Ethics in Publicationcall_made |
Retraction Note by the Editor-in-Chief
Highlights and Novelties
1- As due to large model complexity involved behind deep learning models and architecture, performance of model entirely depends on data scale, dredging and quality.
2- The importance of domain knowledge is crucial in deep learning. It successfully applied in various sectors like manufacturing, cell diagnosis, blood cell type detection which enabled deep learning techniques to provide effective and higher precision results.
3- The transfer learning is way to apply knowledge learned from one domain to another related domain in distinguished sectors.
Manuscript Abstract
In today’s era, due to the emerging growth of computation and automatic disease recognition, capability of deep learning facilitates human life easier. Variety of deep learning models like convolutional neural network (CNN), autoencoders (AE), deep belief networks (DBN) etc. facilitates automated machine health monitoring, and provides better diagnostic results than clinical practitioners. These techniques have developed for a variety of applications like computer vision, document analysis, pattern recognition, image synthesis and syntactic recognition, to mention a few. In this editorial article, we aims at introducing some future trends regarding the utilization of such these deep learning techniques for the recognition and classification of various disease, syndrome and distortions.
Keywords
Deep Learning Disease Recognition Disease Classification Deep Neural Networks
Copyright and Licence
Copyright © International Computer Science and Engineering Society (ICSES). This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution Non Commercial 4.0 International (CC BY-NC 4.0) license, supported by creativecommons.orgcall_made
Cite this manuscript as
Deepak Gupta, Aditya Khamparia, "Future Trends in Utilization of Deep Learning Techniques for Disease Recognition and Classification," ICSES Transactions on Image Processing and Pattern Recognition, vol. 5, no. 1, pp. 1-3, Apr. 2019.
For External Scientific Databeses
--BibTex--
@article{al._270 title="Future Trends in Utilization of Deep Learning Techniques for Disease Recognition and Classification", author="Deepak Gupta", author="Aditya Khamparia", journal="ICSES Transactions on Image Processing and Pattern Recognition (ITIPPR)", volume="5", number="1", pages="1-3", year="2019", month="04", day="30", publisher= "International Computer Science and Engineering Society (ICSES)", doi="", url="http://www.i-cses.com/files/download.php?pID=270"}
--EndNote--
%0 Journal Article %T Future Trends in Utilization of Deep Learning Techniques for Disease Recognition and Classification %A Deepak Gupta %A Aditya Khamparia %J ICSES Transactions on Image Processing and Pattern Recognition (ITIPPR) %V 5 %N 1 %P 1-3 %D 2019 %I International Computer Science and Engineering Society (ICSES) %U http://www.i-cses.com/files/download.php?pID=270 %8 2019-04-30 %R %@ 2645-8071
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