Volume 2, Issue 2, June 2019, Page: 22-26
Deep Learning Applications in the Medical Image Recognition
Song Yukun, High School of Nankai University, Tianjin, China
Received: Apr. 22, 2019;       Accepted: Jun. 2, 2019;       Published: Jul. 26, 2019
DOI: 10.11648/j.ajcst.20190202.11      View  115      Downloads  37
Abstract
In this essay, the researcher is focusing on the deep learning systems and its major applications in various fields. Song Yukun uses the relu incentive algorithm and the convolution functions to make the program automatically recognize different things or same type of things with different features. Before actually processing the image recognition part, the researcher adds a transforming program which change all kinds of image into one small form. Then, using this modelled image, the program could delicately determine the type of the contents in the image. This technological program is automatic and performs as an essential part of artificial intelligences. The main work it does is imitating the learning process of human brain, which accumulate experiences from thousands of events. It realizes this function by adding different algorithms in the program including the relu incentive algorithm which “teaches” the program particular types of images. After massive input, this technological program could quickly solve current problems with the lack of human labor force doing repetitive but intelligent works like checking particular tumor in the X-ray films. Besides, learning by themselves, the programs could generate results more specific than humans do. This deep-learning principle could be widely utilized since everything in human lives are learning and accumulating experiences. It could change any previous mechanical program into “intelligent” programs which would have an acceleration in their delicacy of determination.
Keywords
Deep Learning, Convolutional Neural Network (CNN), Image Recognition
To cite this article
Song Yukun, Deep Learning Applications in the Medical Image Recognition, American Journal of Computer Science and Technology. Vol. 2, No. 2, 2019, pp. 22-26. doi: 10.11648/j.ajcst.20190202.11
Copyright
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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