Open AccessBrain Tumor Classification Based on Enhanced CNN Model
Author(s) -
Naveen Mukkapati,
M.S. Anbarasi
Publication year - 2022
Publication title -
revue d'intelligence artificielle
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.146
H-Index - 14
eISSN - 1958-5748
pISSN - 0992-499X
DOI - 10.18280/ria.360114
Subject(s) - overfitting , computer science , artificial intelligence , segmentation , brain tumor , pattern recognition (psychology) , context (archaeology) , benchmark (surveying) , convolutional neural network , contextual image classification , process (computing) , deep learning , machine learning , artificial neural network , image (mathematics) , pathology , medicine , paleontology , geodesy , biology , geography , operating system
Brain tumor classification is important process for doctors to plan the treatment for patients based on the stages. Various CNN based architecture is applied for the brain tumor classification to improve the classification performance. Existing methods in brain tumor segmentation have the limitations of overfitting and lower efficiency in handling large dataset. In this research, for brain tumor segmentation purpose the enhanced CNN architecture based on U-Net, for pattern analysis purpose RefineNet and for classifying brain tumor purpose SegNet architecture is proposed. The brain tumor benchmark dataset was used to analysis the efficiency of the enhanced CNN model. The U-Net provides good segmentation based on the local and context information of MRI image. The SegNet selects the important features for classification and also reduces the trainable parameters. When compared with the existing methods of brain tumor classification, the enhanced CNN method has the higher performance. The enhanced CNN model has the accuracy of 96.85% and existing CNN with transfer learning has 94.82% accuracy.