@article{MAKHILLIJSC20149321202,
    title = {Classification of Brain Tumor Images using Orthogonal Based Composite Operators and Artmap of Mirror Neurons},
    journal = {International Journal of Soft Computing},
    volume = {9},
    number = {3},
    pages = {189-193},
    year = {2014},
    issn = {1816-9503},
    doi = {ijscomp.2014.189.193},
    url = {https://makhillpublications.co/view-article.php?issn=1816-9503&doi=ijscomp.2014.189.193},
    author = {R. and},
    keywords = {MRI,Adaptive Resonance Theory Mapping (ARTMAP),sobel operator,orthogonal polynomial operator,orthonormal operator},
    abstract = {Tumor as the definition goes is an abnormal growth of cells 
  which can occur in any part of the human body. Such growth when occurs in brain 
  is called brain tumor. Only symptoms and no causes have been found so far. Brain 
  tumors give very normal symptoms like nausea or headache which may occur due 
  to other reasons also. Therefore, early identification of such tumor is very 
  much necessary. Its threat level depends on a combination of various factors 
  like the type of tumor, its location, size and developmental stage. Many techniques 
  exist for scanning the brain like Computed Tomography (CT) scan, Magnetic Resonance 
  Imaging (MRI), etc. to test the tumor existence. MRI is most common one used 
  for used for analyzing the brain as the images produced are of high precision 
  and applicability. The main objective of this study is to classify the brain 
  MRI dataset for the existence or non existence of tumors. The proposed method 
  uses convolution of orthogonal operators with edge detection operators which 
  is applied on the image. Classification of the image is done using ARTMAP of 
  mirror neurons. The classification accuracy is 90% for the proposed method which 
  is better when compared to BPN based classification.}
    }