Multi-Classification of Brain Tumor Images Using Deep Neural Network
Open accessAbstract
Brain tumor classification is a crucial task to evaluate the tumors and make a treatment decision according to their classes. There are many imaging techniques used to detect brain tumors. However, MRI is commonly used due to its superior image quality and the fact of relying on no ionizing radiation. Deep learning (DL) is a subfield of machine learning and recently showed a remarkable performance, especially in classification and segmentation problems. In this paper, a DL model based on a convolutional neural network is proposed to classify different brain tumor types using two publicly available datasets. The former one classifies tumors into (meningioma, glioma, and pituitary tumor). The other one differentiates between the three glioma grades (Grade II, Grade III, and Grade IV). The datasets include 233 and 73 patients with a total of 3064 and 516 images on T1-weighted contrast-enhanced images for the first and second datasets, respectively. The proposed network structure achieves a significant performance with the best overall accuracy of 96.13% and 98.7%, respectively, for the two studies. The results indicate the ability of the model for brain tumor multi-classification purposes.
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The Figshare dataset has been publicly available since 2015.
“This dataset has been publicly available since 2015.”
From A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis (2022) · cites this paper as [42]
The use of two datasets enhanced the accuracy of the proposed method by Sultan et al.
“The use of the two datasets enhanced the accuracy of the proposed method.”
From A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis (2022) · cites this paper as [42]
Sultan et al. proposed a method that enhances the efficiency of clinical experts by providing the multiclassification of brain tumors using MR images.
“sultan et al. [42] proposed a method that enhances the efficiency of clinical experts by providing the multiclassification of brain tumors using MR images.”
From A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis (2022) · cites this paper as [42]
Gliomas and meningiomas are the most common form of tumors and originate from the cerebrum and supportive tissues of the brain.
“Gliomas and meningiomas are the most common form of tumors [43] and originate from the cerebrum and supportive tissues of the brain [42,86].”
From A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis (2022) · cites this paper as [42]
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- NANCY M. SALEM was born in Egypt, in 1975. She received the B.Sc. degree and M.Sc. in electronics and communications engineering from Helwan University, Cairo, Egypt, in 1998 and 2003, respectively, and the Ph.D. degree in engineering from the Department of Electrical Engineering and Electronics, The University of Liverpool, U.K., in 2007. In 2008, she joined the Department of Electronics and Communications Engineering, Helwan University, as an Assistant Professor. She is currently an Associate Professor with the Department of Biomedical Engineering, Helwan University. Her current research interests include signal processing, medical image processing, machine learning, and watermarking and modeling. She is a member with the Institute of Electrical and Electronics Engineers and the Counselor of the Women in Engineering (WIE) and IEEE Helwan Student Branch. She reviewed many scientific papers, proposals and research projects.
- WALID AL-ATABANY received the B.Sc. and M.Sc. degrees from the Biomedical Engineering Department, Cairo University, in 1999 and 2004, respectively, and the Ph.D. degree in biomedi- cal engineering from Imperial College London, in 2010. In 2011, he was a Research Associate with Newcastle University for two years. He is currently an Associate Professor with the Biomed- ical Engineering Department, Helwan University, Egypt. His research interests include signal and image processing (particularly for visually impaired), medical imaging, and modeling of retinal processing. He received the 2nd Price Award from the 2nd Symposium of the Neuroscience Technology Network (NTN2009), the ARVO 2010 travel grant from the AFER/National Institute for Health Research Biomedical Research Centre for Ophthalmology, and two grants from the Newton institutional link grant from the British council, in 2015 and 2016.
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- Authors
- Sultan, H. H., Salem, N. M., & Al-Atabany, W.
- Affiliations
- Helwan University (EG)
- Venue
- IEEE Access
- Published
- 2019
- Volume
- 7
- Pages
- 69215-69225
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2947735999
- Cited by
- 775
- References
- 37
- Citations by year
- 2026: 74 · 2025: 130 · 2024: 141 · 2023: 159 · 2022: 137 · 2021: 93 · 2020: 35 · 2019: 6
- FWCI
- 29.85
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Health Sciences › Medicine › Genetics › Glioma Diagnosis and Treatment
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- brain tumor classification, multi-class classification, convolutional neural networks, meningioma, glioma, pituitary tumors, glioma grade, contrast-enhanced T1-weighted imaging, medical image analysis
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