Convolutional Neural Network-Based MRI Brain Tumor Classification System


  • Murugan Subbiah Product Design Specialist, Embedded System, Vee Eee Technologies, Chennai, Tamil Nadu, India
  • S. Mohan Kumar Dean & Professor, Department of Compute Science and Engineering, Nagarjuna College of Engineering and Technology, Bangalore, India
  • T. R. Ganesh Babu, ME, Ph.D, Professor, Department of Electronics and Communication Engineering, Muthayammal Engineering College, Rasipuram-637408, Namakkal district, Tamil Nadu, India


A brain tumor, the cause of more death rates among all cancers, is diagnosed using uncontrollable cell growth and abnormal brain cell partitioning. The recent progress in Deep Learning (DL) neural network aids the health service in medical image diagnosing. The visual learning of image recognition may result in fault detection and that can be solved using machine learning. The Convolutional Neural Network (CNN) model is used in our study to categorize distinct brain tumor types. There are three main phases namely; image pre-processing, feature extraction and classification. In pre-processing stage, the image processing is done by edge detection and cropping of MRI brain images. Then feature extraction employing the Transfer Learning (TL) approach is followed by CNN model classifier layers for the classification of brain tumor images. The experimental results demonstrate that our model is extremely effective at minimal computing power with less complexity. In the performance comparison of our suggested CNN model with VGG16, achieves greater accuracy even with less dataset.


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