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Early Brain Tumor Detection Using Fuzzy Based Methodology-Mamdani

EasyChair Preprint no. 9637

6 pagesDate: January 30, 2023


The aim of proposed method is to reduce timetaken for brain tumor diagnosis. As the inflexible skull encloses the brain, making any growth within this confined area dangerous. Detecting brain tumor accurately is very difficult, even for a medical expert. Early detection is needed to detect tumor analysis results effectively. The recommended solution is Mamdani's K-means clustering method and morphological operations and to overcome this limitation in portion The solution is The K-means clustering method is used to distribute into various groups, which are used in calculating mode and mean-edge mode,while morphological operations include an Anisotropic Diffusion Filter, which then  enumerates pixel counts. They are provided as input to the Mamdani Inference system. The combination of anisotropic diffusion filter and K-means clustering method results in less time consumption. Also, a comparison of the Sobel, Canny,and Prewitt filtershas been studied for the brain.

Keyphrases: Brain Tumor, k-means clustering method, Mamdani Inference System, Sobel edge detection method

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Kanza Fatima and Lubna Moin},
  title = {Early Brain Tumor Detection Using Fuzzy Based Methodology-Mamdani},
  howpublished = {EasyChair Preprint no. 9637},

  year = {EasyChair, 2023}}
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