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Performance Analysis of the Deep Learning Method for Medical Cases

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2021
Dian Pratiwi, Anung B. Ariwibowo, Dimmas Mulya

Dian Pratiwi, Anung B Ariwibowo and Dimmas Mulya. Performance Analysis of the Deep Learning Method for Medical Cases. International Journal of Computer Applications 183(1):32-37, May 2021. BibTeX

	author = {Dian Pratiwi and Anung B. Ariwibowo and Dimmas Mulya},
	title = {Performance Analysis of the Deep Learning Method for Medical Cases},
	journal = {International Journal of Computer Applications},
	issue_date = {May 2021},
	volume = {183},
	number = {1},
	month = {May},
	year = {2021},
	issn = {0975-8887},
	pages = {32-37},
	numpages = {6},
	url = {},
	doi = {10.5120/ijca2021921275},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


The development of technology is moving very rapidly, especially in the medical field. This is due to the increasing number of cases of disease requiring the diagnostic ability of a Medical Expert to analyze, but not all Medical Personnel can carry out an in-depth analysis with a Medical Expert. Therefore, a lot of research has been carried out to make technology to help in medical process. Having research that can mimic a medical expert's ability to diagnose disease can increase the chances of a patient being saved before it's too late. There are many methods used in research to assist in the process of diagnosing a patient's disease. Deep learning method is the one that is used. Deep Learning method is split up into several derived methods, that methods are Deep Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks. With analyzing abilities each derived methods, it can make it easier for further research to create or develop better research than before in order to advance technology in the medical field...


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Artificial Intelligence, Deep Learning, Disease Diagnosis, Deep Neural Network, Convolutional Neural Network, Recurrent Neural Network.