The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
Multi-grade brain tumor classification using deep CNN with extensive data augmentation
Abstract
Abstract Numerous computer-aided diagnosis (CAD) systems have been recently presented in the history of medical imaging to assist radiologists about their patients. For full assistance of radiologists and better analysis of magnetic resonance imaging (MRI), multi-grade classification of brain tumor is an essential procedure. In this paper, we propose a novel convolutional neural network (CNN) based multi-grade brain tumor classification system. Firstly, tumor regions from an MR image are segmented using a deep learning technique. Secondly, extensive data augmentation is employed to effectively train the proposed system, avoiding the lack of data problem when dealing with MRI for multi-grade brain tumor classification. Finally, a pre-trained CNN model is fine-tuned using augmented data for brain tumor grade classification. The proposed system is experimentally evaluated on both augmented and original data and results show its convincing performance compared to existing methods.
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Cites
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Cited by
Convolutional neural networks in medical image understanding: a survey
Sarvamangala, D. R., & Kulkarni, R. V. (2022) · Evolutionary Intelligence · doi:10.1007/s12065-020-00540-3 · Cited by 917
A Deep Learning Model Based on Concatenation Approach for the Diagnosis of Brain Tumor
Noreen, N., Palaniappan, S., Qayyum, A., Ahmad, I., Imran, M., & Shoaib, M. (2020) · IEEE Access · doi:10.1109/access.2020.2978629 · Cited by 478
Deep CNN for Brain Tumor Classification
Ayadi, W., Elhamzi, W., Charfi, I., & Atri, M. (2021) · Neural Processing Letters · doi:10.1007/s11063-020-10398-2 · Cited by 448
Deep Multi-Scale 3D Convolutional Neural Network (CNN) for MRI Gliomas Brain Tumor Classification
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Literature review: efficient deep neural networks techniques for medical image analysis
Abdou, M. A. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-06960-9 · Cited by 312
Metadata
- Authors
- Sajjad, M., Khan, S., Muhammad, K., Wu, W., Ullah, A., & Baik, S. W.
- Venue
- Journal of Computational Science
- Publisher
- Elsevier BV
- Published
- 2019-01
- Volume
- 30
- Pages
- 174-182
- ISSN
- 1877-7503 (ISSN-L)
- OpenAlex
- W2905017682
- Cited by
- 891
- References
- 43
- FWCI
- 20.78
- Type
- journal article
- Language
- en
- Open access
- closed
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