DETECTION OF INTRACRANIAL TUMOR USING CONVOLUTION NEURAL NETWORK

DETECTION OF INTRACRANIAL TUMOR USING CONVOLUTION NEURAL NETWORK

Dr. P. Visu¹, K Swetha², V Priya Darshini² and S Meena²

¹Professor, Department of Computer Science Engineering, Velammal Engineering College, Ambattur Red Hills Road,

Chennai, Tamil Nadu 600066.

²UG Scholars, Department of Computer Science Engineering, Velammal Engineering College, Ambattur Red Hills Road,

Chennai, Tamil Nadu 600066.

Abstract - Brain tumor image classification is a vital a part of medical image processing. It assists doctors to create accurate diagnosis and treatment plans. resonance (MR) imaging is one among the most imaging tools to review brain tissue. In our proposed system propose the neoplasm MR image classification method using transfer learning-based CNN-pretrained VGG-16. When these algorithms are applied on the MRI images the detection of neoplasm is completed in no time and the next accuracy helps in providing the treatment to the patients. This detection also helps the radiologist in making quick decisions. As an outcome, we will estimate that the pretrained model VGG-16 determines highly adequate results with a rise within the accuracy rate of coaching and validation.

Keywords - Brain Tumor, Detection, Classification, MRI images, CNN, VGG-16.