Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Self Organization Map based Texture Feature Extraction for Efficient
  Medical Image Categorization   Texture is one of the most important properties of visual surface that helps
in discriminating one object from another or an object from background. The
self-organizing map (SOM) is an excellent tool in exploratory phase of data
mining. It projects its input space on prototypes of a low-dimensional regular
grid that can be effectively utilized to visualize and explore properties of
the data. This paper proposes an enhancement extraction method for accurate
extracting features for efficient image representation it based on SOM neural
network. In this approach, we apply three different partitioning approaches as
a region of interested (ROI) selection methods for extracting different
accurate textural features from medical image as a primary step of our
extraction method. Fisherfaces feature selection is used, for selecting
discriminated features form extracted textural features. Experimental result
showed the high accuracy of medical image categorization with our proposed
extraction method. Experiments held on Mammographic Image Analysis Society
(MIAS) dataset.

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The categories of this paper are: dg-ga
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
2
The categories of this paper are: math
5
The categories of this paper are: gr-qc
15
The categories of this paper are: nucl-ex
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The categories of this paper are: acc-phys
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The categories of this paper are: q-bio
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The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
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The categories of this paper are: alg-geom
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The categories of this paper are: adap-org
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The categories of this paper are: econ
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The categories of this paper are: patt-sol