Open AccessHigh precision brain tumor classification model based on deep transfer learning and stacking concepts
Author(s) -
Halima El Hamdaoui,
Anass Benfares,
Saïd Boujraf,
Nour El Houda Chaoui,
Badreeddine Alami,
Mustapha Maâroufi,
Hassan Qjidaa
Publication year - 2021
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v24.i1.pp167-177
Subject(s) - convolutional neural network , computer science , artificial intelligence , transfer of learning , deep learning , stacking , brain tumor , segmentation , artificial neural network , test data , sensitivity (control systems) , pattern recognition (psychology) , glioma , machine learning , medicine , pathology , physics , nuclear magnetic resonance , cancer research , programming language , electronic engineering , engineering
In this article, we proposed an intelligent clinical decision support system for the detection and classification of brain tumor from risk of malignancy index (RMI) images. To overcome the lack of labeled training data needed to train convolutional neural networks, we have used a deep transfer learning and stacking concepts. For this, we choosed seven convolutional neural networks (CNN) architectures already pre-trained on an ImageNet dataset that we precisely fit on magnetic resonance imaging (MRI) of brain tumors collected from the brain tumor segmentation (BraTS) 19 database. To improve the accuracy of our global model, we only predict as output the prediction that obtained the maximum score among the predictions of the seven pre-trained CNNs. We used a 10-way cross-validation approach to assess the performance of our main 2-class model: low-grade glioma (LGG) and high-grade glioma (HGG) brain tumors. A comparison of the results of our proposed model with those published in the literature, shows that our proposed model is more efficient than those published with an average test precision of 98.67%, an average f1 score of 98.62%, a test precision average of 98.06% and an average test sensitivity of 98.33%.