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

Deepak Gupta a,, Aditya Khamparia b
a Maharaja Agrasen Institute of Technology (MAIT), Delhi, India
b Lovely Professional University, Phagwara, India

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
© Copyright was transferred to International Computer Science and Engineering Society (ICSES) by all the Authors.

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 (ITIPPR), vol. 5, no. 1, pp. 1-3, Apr. 2019.

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