Decision-level fusion scheme for nasopharyngeal carcinoma identification using machine learning techniques-Reference-Cited by-同舟云学术

Decision-level fusion scheme for nasopharyngeal carcinoma identification using machine learning techniques

Published:2018-11-19 Issue:3 Volume:32 Page:625-638
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Abd Ghani Mohd Khanapi,Mohammed Mazin Abed^ORCID,Arunkumar N.,Mostafa Salama A.,Ibrahim Dheyaa Ahmed,Abdullah Mohamad Khir,Jaber Mustafa Musa,Abdulhay Enas,Ramirez-Gonzalez Gustavo,Burhanuddin M. A.

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

http://link.springer.com/content/pdf/10.1007/s00521-018-3882-6.pdf

Reference30 articles.

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2. Mohammed MA, Ghani MKA, Hamed RI, Ibrahim DA (2017) Analysis of an electronic methods for nasopharyngeal carcinoma: prevalence, diagnosis, challenges and technologies. J Comput Sci 21:241–254

3. Siegel RL, Miller KD, Jemal A (2016) Cancer statistics, 2016. CA Cancer J Clin 66(1):7–30

4. Mohammed MA, Ghani MKA, Hamed RI, Abdullah MK, Ibrahim DA (2017) Automatic segmentation and automatic seed point selection of nasopharyngeal carcinoma from microscopy images using region growing based approach. J Comput Sci 20:61–69

5. Mohammed MA, Ghani MKA, Arunkumar N, Mostafa SA, Burhanuddin MA (2018) Trainable model for segmenting and identifying Nasopharyngeal carcinoma. Comput Electr Eng 71:372–387. https://doi.org/10.1016/j.compeleceng.2018.07.044

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