Cited by 310Brain Tumor Detection and ClassificationAI in cancer detectionGlioma Diagnosis and Treatment

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BrainMRNet: Brain tumor detection using magnetic resonance images with a novel convolutional neural network model

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Toğaçar, Mesut, Ergen, Burhan, Cömert, Zafer·2020·Medical Hypotheses

DOI 10.1016/j.mehy.2019.109531

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Abstract

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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Authors: Toğaçar, M., Ergen, B., & Cömert, Z.
Affiliations: Fırat University (TR)
Samsun University (TR)
Corresponding author: Toğaçar, Mesut
ORCID: Toğaçar, Mesut
Ergen, Burhan
Cömert, Zafer
Venue: Medical Hypotheses
Publisher: Elsevier BV
Published: 2020-01
Volume: 134
Pages: 109531
ISSN: 0306-9877 (ISSN-L)
DOI: 10.1016/j.mehy.2019.109531
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: cc-by-nc-nd
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
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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BrainMRNet: Brain tumor detection using magnetic resonance images with a novel convolutional neural network model

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