Volume 9 Issue 1 - September 2017

  • 1. Brain tumor detection in magnetic resonance images

    Authors : Balkrishan Jindal, Damanpreet Singh

    Pages : 97-104

    DOI : http://dx.doi.org/10.21172/1.91.15

    Keywords : MRI Brain Tumour, Image Segmentation, Feature Extraction, Feed Forward Neural Network, Mean Square Error

    Abstract :

    Magnetic Resonance Imaging (MRI) of the brain is a painless and secure method in medical field. It is used to create images of human body for diagnosis of various diseases in the human body. In this paper, brain tumor is detected based on Gray level co-occurrence matrix used for computation of energy, contrast and homogeneity. Segmentation is used to detect location, shape and size of the brain tumor in MRI images. These features are further used for training the Feed Forward Neural Network (FFNN) for classify the image. The performance of the proposed method evaluated using parameters like Area, Perimeter, Contrast, Energy, Homogeneity, Mean Square Error and Confusion Matrix. The experimental results of the proposed method are also compared with Back Propagation Neural Network. From the experimental results it has been concluded that the performance parameters of the proposed method are better than Back Propagation Neural Network.

    Citing this Journal Article :

    Balkrishan Jindal, Damanpreet Singh, "Brain tumor detection in magnetic resonance images", https://www.ijltet.org/journal_details.php?id=921&j_id=3979, Volume 9 Issue 1 - September 2017, 97-104, #ijltetorg