A computer-aided grading of glioma tumor using deep residual networks fusion.
Convolutional neural network
Deep learning
Glioma
Magnetic resonance imaging
Journal
Computer methods and programs in biomedicine
ISSN: 1872-7565
Titre abrégé: Comput Methods Programs Biomed
Pays: Ireland
ID NLM: 8506513
Informations de publication
Date de publication:
Mar 2022
Mar 2022
Historique:
received:
29
12
2020
revised:
19
10
2021
accepted:
20
12
2021
pubmed:
3
1
2022
medline:
17
2
2022
entrez:
2
1
2022
Statut:
ppublish
Résumé
Among different cancer types, glioma is considered as a potentially fatal brain cancer that arises from glial cells. Early diagnosis of glioma helps the physician in offering effective treatment to the patients. Magnetic Resonance Imaging (MRI)-based Computer-Aided Diagnosis for the brain tumors has attracted a lot of attention in the literature in recent years. In this paper, we propose a novel deep learning-based computer-aided diagnosis for glioma tumors. The proposed method incorporates a two-level classification of gliomas. Firstly, the tumor is classified into low-or high-grade and secondly, the low-grade tumors are classified into two types based on the presence of chromosome arms 1p/19q. The feature representations of four residual networks have been used to solve the problem by utilizing transfer learning approach. Furthermore, we have fused these trained models using a novel Dempster-shafer Theory (DST)-based fusion scheme in order to enhance the classification performance. Extensive data augmentation strategies are also utilized to avoid over-fitting of the discrimination models. Extensive experiments have been performed on an MRI dataset to show the effectiveness of the method. It has been found that our method achieves 95.87% accuracy for glioma classification. The results obtained by our method have also been compared with different existing methods. The comparative study reveals that our method not only outperforms traditional machine learning-based methods, but it also produces better results to state-of-the-art deep learning-based methods. The fusion of different residual networks enhances the tumor classification performance. The experimental findings indicates that Dempster-shafer Theory (DST)-based fusion technique produces superior performance in comparison to other fusion schemes.
Sections du résumé
BACKGROUND AND OBJECTIVES
OBJECTIVE
Among different cancer types, glioma is considered as a potentially fatal brain cancer that arises from glial cells. Early diagnosis of glioma helps the physician in offering effective treatment to the patients. Magnetic Resonance Imaging (MRI)-based Computer-Aided Diagnosis for the brain tumors has attracted a lot of attention in the literature in recent years. In this paper, we propose a novel deep learning-based computer-aided diagnosis for glioma tumors.
METHODS
METHODS
The proposed method incorporates a two-level classification of gliomas. Firstly, the tumor is classified into low-or high-grade and secondly, the low-grade tumors are classified into two types based on the presence of chromosome arms 1p/19q. The feature representations of four residual networks have been used to solve the problem by utilizing transfer learning approach. Furthermore, we have fused these trained models using a novel Dempster-shafer Theory (DST)-based fusion scheme in order to enhance the classification performance. Extensive data augmentation strategies are also utilized to avoid over-fitting of the discrimination models.
RESULTS
RESULTS
Extensive experiments have been performed on an MRI dataset to show the effectiveness of the method. It has been found that our method achieves 95.87% accuracy for glioma classification. The results obtained by our method have also been compared with different existing methods. The comparative study reveals that our method not only outperforms traditional machine learning-based methods, but it also produces better results to state-of-the-art deep learning-based methods.
CONCLUSION
CONCLUSIONS
The fusion of different residual networks enhances the tumor classification performance. The experimental findings indicates that Dempster-shafer Theory (DST)-based fusion technique produces superior performance in comparison to other fusion schemes.
Identifiants
pubmed: 34974232
pii: S0169-2607(21)00671-4
doi: 10.1016/j.cmpb.2021.106597
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
106597Informations de copyright
Copyright © 2021 Elsevier B.V. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Competing Interest Authors declare that they have no conflict of interest.