The DOI matches its Crossref record; 46 of the 47 references it cites were found in a registry or an index.
BrainMRNet: Brain tumor detection using magnetic resonance images with a novel convolutional neural network model
Open accessAbstract
A brain tumor is a mass that grows unevenly in the brain and directly affects human life. This mass occurs spontaneously because of the tissues surrounding the brain or the skull. Surgical methods are generally preferred for the treatment of the brain tumor. Recently, models of deep learning in the diagnosis and treatment of diseases in the biomedical field have gained intense interest. In this study, we propose a new convolutional neural network model named BrainMRNet. This architecture is built on attention modules and hypercolumn technique; it has a residual network. Firstly, image is preprocessed in BrainMRNet. Then, this step is transferred to attention modules using image augmentation techniques for each image. Attention modules select important areas of the image and the image is transferred to convolutional layers. One of the most important techniques that the BrainMRNet model uses in the convolutional layers is hypercolumn. With the help of this technique, the features extracted from each layer of the BrainMRNet model are retained by the array structure in the last layer. The aim is to select the best and the most efficient features among the features maintained in the array. Accessible magnetic resonance images were used to detect brain tumor with the BrainMRNet model. BrainMRNet model is more successful than the pre-trained convolutional neural network models (AlexNet, GoogleNet, VGG-16) used in this study. The classification success achieved with the BrainMRNet model was 96.05%.
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In the citing paper
“The total number of images was 253, and patients provided each image voluntarily, making the dataset heterogeneous.”
From A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis (2022) · cites this paper as [59]
In the citing paper
“The images were not of a uniform resolution, and the image quality was not high, reducing the efficiency 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 [59]
Citations
Cites
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Shinde, R., Patil, S. H., Kotecha, K. V., & Mishra, S. (2026) · Scientific Reports · doi:10.1038/s41598-026-44040-3
Parshapa, P., & Rani, P. I. (2026) · Radio Science · doi:10.1029/2025rs008514
ResNet with Channel Attention for Efficient Brain Tumor Classification Using MRI
Shivakumar., E., & Jayaprakash, M. (2026) · Contemporary Computing Innovations Conference (CCIC) · doi:10.1109/ccic68129.2026.11486048
Optimizing Brain Tumor Classification for Edge Devices with Knowledge Distillation
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Robust MRI Image Segmentation: Leveraging the Power of KGM Optimized W-FCM with Deep Learning
Anantharajan, S., Gunasekaran, S., Anantharajan, S., & Subramanian, T. (2026) · SN Computer Science · doi:10.1007/s42979-026-05075-5
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A Novel Deep Learning Framework for Automated Brain Tumor Classification Based on CNN-BiLSTM Model
Saraswathi, C., & Chakradhar, K. S. (2026) · International Research Journal of Multidisciplinary Technovation · doi:10.54392/irjmt2648
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Metadata
- Authors
- Toğaçar, M., Ergen, B., & Cömert, Z.
- Affiliations
- Fırat University (TR)
- Samsun University (TR)
- Corresponding author
- Toğaçar, Mesut
- Venue
- Medical Hypotheses
- Publisher
- Elsevier BV
- Published
- 2020-01
- Volume
- 134
- Pages
- 109531
- ISSN
- 0306-9877 (ISSN-L)
- OpenAlex
- W2995003683
- Cited by
- 310
- References
- 47
- Citations by year
- 2026: 12 · 2025: 54 · 2024: 71 · 2023: 76 · 2022: 49 · 2021: 33 · 2020: 14
- FWCI
- 10.69
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- Medical Hypotheses (publishedVersion)
- PubMed (publishedVersion)
- DSpace - FIRAT (Fırat University) (submittedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Brain Tumor Detection and Classification
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Oncology › Glioma Diagnosis and Treatment
- Keywords
- brain tumor detection, magnetic resonance imaging, convolutional neural networks, attention modules, hypercolumns, ResNet, image augmentation, medical image classification, feature selection
- MeSH terms
- Deep Learning, Algorithms, Brain Neoplasms, Diagnosis, Computer-Assisted, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Signal Processing, Computer-Assisted, Early Detection of Cancer, Neuroimaging, Datasets as Topic
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