American Journal of Computer Science and Technology

Submit a Manuscript

Publishing with us to make your research visible to the widest possible audience.

Propose a Special Issue

Building a community of authors and readers to discuss the latest research and develop new ideas.

Deep Learning Applications in the Medical Image Recognition

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.

Deep Learning, Convolutional Neural Network (CNN), Image Recognition

Song Yukun. (2019). Deep Learning Applications in the Medical Image Recognition. American Journal of Computer Science and Technology, 2(2), 22-26.

Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. Abien Fred M. Agarap, arXiv: 1803.08375.
2. Hidenori Ide; Takio Kurita, Proceeding of 2017 International Joint Conference on Neural Networks (IJCNN).
3. Huan Zhang, Tsui-Wei Weng, Pin-Yu Chen, Cho-Jui Hsieh, Luca Daniel, Proceeding of Thirty-second Conference on Neural Information Processing Systems.
4. Siamak Ravanbakhsh, Barnabas Poczos, Jeff Schneider, Dale Schuurmans, Russell Greiner; Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, PMLR 51: 809-818, 2016.
5. Gregory Koch, Richard Zemel, Ruslan Salakhutdinov, Proceedings of the 32 nd International Conference on Machine Learning, Lille, France, 2015. JMLR: W&CP volume 37.
6. Vidushi Sharma, Sachin Rai, Anurag Dev, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 2, Issue 10.
7. Juergen Schmidhuber, Neural Networks, Vol 61, pp 85-117, Jan 2015.
8. Waseem Rawat, Zenghui Wang, Neural Computation 29, 2352–2449 (2017).
9. Saad Albawi; Tareq Abed Mohammed, Saad Al-Zawi, proceeding of 2017 International Conference on Engineering and Technology (ICET).
10. Wenling Shang, Kihyuk Sohn, Diogo Almeida, Honglak Lee, Proceedings of the 33 rd International Conference on Machine Learning, New York, NY, USA, 2016. JMLR: W&CP volume 48.
11. Hayoung Eom, Heeyoul Choi, proceeding of ICASSP 2019.
12. Jawad Nagi, et al, proceeding of 2011 IEEE International Conference on Signal and Image Processing Applications (ICSIPA).
13. Manli Su, Zhanjie Song, et al, Neurocomputing, Volume 224, 8 February 2017, Pages 96-104.
14. Qi Zhao, Shuchang Lyu, et al, Wireless Communications and Mobile Computing, Volume 2018, Article ID 8196906, 15 pages.
15. Chen-Yu Lee, Patrick W. Gallagher, Zhuowen Tu, Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS) 2016, Cadiz, Spain. JMLR: W&CP volume 51.
16. Dingjun Yu, Hanli Wang, Peiqiu Chen, Zhihua Wei, Proceeding of International Conference on Rough Sets and Knowledge Technology.
17. Jawad Nagi, Frederick Ducatelle, et al, Proceeding of 2011 IEEE International Conference on Signal and Image Processing Applications.
18. Li Deng, Ossama Abdel-Hamid, Dong Yu, IEEE International Conference on Acoustics, Speech, and Signal Processing, May 2013.
19. Ossama Abdel-Hamid, Abdel-rahman Mohamed, Hui Jiang, Li Deng, Gerald Penn, and Dong Yu, IEEE/ACM TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 22, NO. 10, OCTOBER 2014.
20. Yoon Kim, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1746–1751.
21. Sameer Khan, Suet-Peng Yong, Proceedings of APSIPA Annual Summit and Conference 2017.
22. Ruiting Shao, Yang Lei, Jian Fan, Jerry Liu, Proceeding of IS&T International Symposium on Electronic Imaging 2018.
23. Mathias Niepert, Mohamed Ahmed, Konstantin Kutzkov, Proceedings of the 33 rd International Conference on Machine Learning, New York, NY, USA, 2016.
24. Eduardo Carabez, Miho Sugi, Isao Nambu, Yasuhiro Wada, Computational Intelligence and Neuroscience, Volume 2017, Article ID 8163949.
25. Nima Tajbakhsh; Jae Y. Shin, proceeding of IEEE Transactions on Medical Imaging (Volume: 35, Issue: 5, May 2016).
26. Dinggang Shen, Guorong Wu, Heung-Il Suk, Annual Review of Biomedical Engineering, Vol. 19: 221-248 (Volume publication date June 2017).
27. Onur Ozdemir, Benjamin Woodward, Andrew A. Berlin, proceeding of Second workshop on Bayesian Deep Learning (NIPS 2017), Long Beach, CA, USA.
28. Nikou Gunnemann, Jurgen Pfeffer, Proceedings of Machine Learning Research 74: 92–102, 2017.
29. Holger R. Roth, Le Lu, Jiamin Liu, Jianhua Yao, Ari Seff, Kevin Cherry, Lauren Kim, and Ronald M. Summers, Proceeding of IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 35, NO. 5, MAY 2016.
30. Edward Choi, Andy Schuetz, Walter F Stewart, Jimeng Sun, Journal of the American Medical Informatics Association, Volume 24, Issue 2, March 2017, Pages 361–370,
31. Le Lu, Yefeng Zheng, Gustavo Carneiro, Lin Yang, book < Deep Learning and Convolutional Neural Networks for Medical Image Computing >.