Open AccessA Brain Net Classification Technique Based on Deep Convolutional Neural Network for Detection of Brain Tumor in FLAIR MRI Images
Author(s) -
T.H. Manoj*,
M. Gunasekaran,
W. Jaisingh
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.a1424.109119
Subject(s) - artificial intelligence , convolutional neural network , computer science , segmentation , deep learning , pattern recognition (psychology) , field (mathematics) , contextual image classification , fluid attenuated inversion recovery , artificial neural network , key (lock) , brain tumor , machine learning , image (mathematics) , magnetic resonance imaging , radiology , medicine , mathematics , computer security , pathology , pure mathematics
Classification process plays a key role in diagnosing brain tumors. Earlier research works are intended for identifying brain tumors using different classification techniques. The logical gap between the visual representation of data captured by MRI device and the information apparent to the person evaluating poses a key challenge in the medical field. Research in computerized segmentation of tumor is widely gaining popularity nowadays, which may lead to an accurate analysis of MRI images and planned treatment of patients. The recent field of deep learning and neural networks promises to classify images with higher accuracy. This work proposes a new BrainNet classification technique that combines fuzzy c means, morphological operators and CNN to identify image regions that are suspicious. The proposed method is assessed with the help of imaging data obtained from Multimodal Brain Tumor Image Segmentation Challenge (BRATS) 2015 and IXI dataset. The effectiveness of the proposed method is computed with traditional machine learning and Convolutional Neural Networks. Experimental results show that our proposed method outperforms state-of-the-art classification on the BRATS 2015 dataset.