@article{MAKHILLIJSC20138321142,
    title = {Comparative Analysis of Classifier Performance on Medical Image Diagnosis},
    journal = {International Journal of Soft Computing},
    volume = {8},
    number = {3},
    pages = {199-206},
    year = {2013},
    issn = {1816-9503},
    doi = {ijscomp.2013.199.206},
    url = {https://makhillpublications.co/view-article.php?issn=1816-9503&doi=ijscomp.2013.199.206},
    author = {Akila and},
    keywords = {Brain tumor,MRI,feature extraction,classification,binary,association rule,pruning},
    abstract = {This study aims to reveal a comparative analysis of classifier 
  performance on medical image diagnosis, particularly for brain tumor detection 
  and classification. The detection of brain tumor stands in need of Magnetic 
  Resonance Imaging (MRI). The moment invariant feature extraction has been evaluated 
  to categorize the MRI Slices as Normal, Benign and Malignant by Neural Network 
  Classifier. In the comparative study, researchers examine the precision rate 
  of aforementioned classification with extracted features and the classification 
  of brain images with selective features by association rule based neural network 
  classifier. The results are then analyzed with Receiver Operating Characteristics 
  (ROC) curve and compared to illustrate the method producing higher accuracy 
  rate in tumor recognition. Factually, the analysis proves that the classifier 
  research under feature extraction followed by rule pruning method affords better 
  accuracy rate.}
    }