Journal of Cognitive Human-Computer Interaction
  JCHCI
  2771-1463
  2771-1471
  
   10.54216/JCHCI
   https://www.americaspg.com/journals/show/947
  
 
 
  
   2021
  
  
   2021
  
 
 
  
   An Implementation Of Statistical Feature Algorithms For The Detection Of Brain Tumor
  
  
   Department of Computer Science and Engineering,  Panimalar Engineering College, India
   
    P.
    Kavitha
   
   Department of Computer Science and Engineering, Panimalar Engineering College, India	
   
    R. Subha
    Shini
   
   Department of Computer Science and Engineering, Panimalar Engineering College, India
   
    R.
    Priya
   
  
  
   A member of a population who is at risk of becoming infected by disease is a susceptible individual. Finding disease susceptibility and generating an alert in advance, is valuable for an individual. The aim of the work presented a feature vector using different statistical texture analyses of brain tumors from an MRI image. The statistical feature texture is computed using GLCM (Gray Level Co-occurrence Matrices) of brain tumor cell structure. For this paper, the brain tumor cell segmented using the strip method to implement hybrid Assured Convergence Particle Swarm Optimization (ACPSO) - Fuzzy C-means clustering (FCM). Furthermore, the four angles 0o, 45o, 90o, and 135o have calculated the segmented brain image in GLCM. The four angular directions are calculated using texture features are correlation, energy, contrast and homogeneity. The texture analysis is performed on different types of images using past years. So, the algorithm proposed statistical texture features are calculated for iterative image segmentation. The algorithm FETC (Feature Extraction Tumor Cell) extracts statistical features of GLCM. These results show that MRI images can be implemented in a system of brain cancer detection. 
  
  
   2021
  
  
   2021
  
  
   57 
    62
  
  
   10.54216/JCHCI.010202
   https://www.americaspg.com/articleinfo/25/show/947