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AN INTELLIGENT GRADING OF ASTROCYTOMAS BRAIN TUMORS THROUGH IMPROVED HARISH HAWKS OPTIMIZED DEEP CNN

  • Unique Paper ID: 156810
  • Volume: 9
  • Issue: 5
  • PageNo: 56-64
  • Abstract:
  • Brain cancer continues to be among the leading causes of death for both men and women and much effort have been expended in the form of screening programs for prevention. Given the exponential growth in the number of MRI collected by these programs, computer-assisted diagnosis has become a necessity. Computer-assisted detection techniques developed to date to improve diagnosis without multiple systematic readings have not resulted in a significant improvement in performance measures. In this context, the use of automatic image processing techniques resulting from deep learning represents a promising avenue for assisting in the diagnosis of brain cancer. In this paper, present a deep learning approach based on a Convolutional Neural Network (CNN) model for brain tumor classification. The proposed approach aims to classify the brain tumors in to different grades. Experimental results on MRI images using the BRATS dataset show that the HHO-CNN model achieved high processing performances with 97% of accuracy in the proposed classification task when compared with other classification techniques and state-of-the-art models.)
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 9
  • Issue: 5
  • PageNo: 56-64

AN INTELLIGENT GRADING OF ASTROCYTOMAS BRAIN TUMORS THROUGH IMPROVED HARISH HAWKS OPTIMIZED DEEP CNN

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