Open AccessBrain Tumor Detection and Classification Using Deep Learning Techniques based on MRI Images
Author(s) -
B. Kokila,
M S Devadharshini,
A. Anitha,
S. Sankar
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1916/1/012226
Subject(s) - brain tumor , artificial intelligence , computer science , convolutional neural network , identification (biology) , context (archaeology) , magnetic resonance imaging , deep learning , pattern recognition (psychology) , task (project management) , segmentation , radiology , medicine , pathology , engineering , paleontology , botany , systems engineering , biology
The application of deep learning approaches in context to improve health diagnosis is providing impactful solutions. According to the World Health Organization (WHO), proper brain tumor diagnosis involves detection, brain tumor location identification, and classification of the tumor on the basis of malignancy, grade, and type. This experimental work in the diagnosis of brain tumors using Magnetic Resonance Imaging (MRI) involves detecting the tumor, classifying the tumor in terms of grade, type, and identification of tumor location. This method has experimented in terms of utilizing one model for classifying brain MRI on different classification tasks rather than an individual model for each classification task. The Convolutional Neural Network (CNN) based multi-task classification is equipped for the classification and detection of tumors. The identification of brain tumor location is also done using a CNN-based model by segmenting the brain tumor.