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An Adaptive Learning and Classifier Model in MRI Tumor Detection

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Somashekhar Swamy, P. K. Kulkarni

Somashekhar Swamy and P K Kulkarni. An Adaptive Learning and Classifier Model in MRI Tumor Detection. International Journal of Computer Applications 175(5):32-38, October 2017. BibTeX

	author = {Somashekhar Swamy and P. K. Kulkarni},
	title = {An Adaptive Learning and Classifier Model in MRI Tumor Detection},
	journal = {International Journal of Computer Applications},
	issue_date = {October 2017},
	volume = {175},
	number = {5},
	month = {Oct},
	year = {2017},
	issn = {0975-8887},
	pages = {32-38},
	numpages = {7},
	url = {},
	doi = {10.5120/ijca2017915546},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


In the process of image coding, external noises impact a lot in processing efficiency. In the application of medical image processing, this effect is more, important due to its finer content details. It is required to minimize the noise effect with preserving the image content information, without losing the image generality. Towards the objective of image denoising, in this work, a dynamic block coding approach for noise minimization in medical image processing is presented. The filtration approach is an enhancement to the objective of noise elimination using median filtration. The suggested approach, improves the retrieval accuracy more effectively under variant noise condition in consideration to conventional filtration approach.


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Denoising, medical image processing, dynamic block coding, MRI images.