The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
Multi-Classification of Brain Tumor MRI Images Using Deep Convolutional Neural Network with Fully Optimized Framework
Open accessAbstract
Brain tumor diagnosis and classification still rely on histopathological analysis of biopsy specimens today. The current method is invasive, time-consuming and prone to manual errors. These disadvantages show how essential it is to perform a fully automated method for multi-classification of brain tumors based on deep learning. This paper aims to make multi-classification of brain tumors for the early diagnosis purposes using convolutional neural network (CNN). Three different CNN models are proposed for three different classification tasks. Brain tumor detection is achieved with 99.33% accuracy using the first CNN model. The second CNN model can classify the brain tumor into five brain tumor types as normal, glioma, meningioma, pituitary and metastatic with an accuracy of 92.66%. The third CNN model can classify the brain tumors into three grades as Grade II, Grade III and Grade IV with an accuracy of 98.14%. All the important hyper-parameters of CNN models are automatically designated using the grid search optimization algorithm. To the best of author’s knowledge, this is the first study for multi-classification of brain tumor MRI images using CNN whose almost all hyper-parameters are tuned by the grid search optimizer. The proposed CNN models are compared with other popular state-of-the-art CNN models such as AlexNet, Inceptionv3, ResNet-50, VGG-16 and GoogleNet. Satisfactory classification results are obtained using large and publicly available clinical datasets. The proposed CNN models can be employed to assist physicians and radiologists in validating their initial screening for brain tumor multi-classification purposes.
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Cites
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The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository
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Brain tumor classification using deep CNN features via transfer learning
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Multi-grade brain tumor classification using deep CNN with extensive data augmentation
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Multi-Classification of Brain Tumor Images Using Deep Neural Network
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Classification of Brain Tumors from MRI Images Using a Convolutional Neural Network
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A Deep Learning-Based Framework for Automatic Brain Tumors Classification Using Transfer Learning
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Cited by
Accurate brain tumor detection using deep convolutional neural network
Khan, M. S. I., Rahman, A., Debnath, T., Karim, M. R., Nasir, M. K., Band, S. S., Mosavi, A., & Dehzangi, I. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.08.039 · Cited by 401
A deep learning approach for brain tumor classification using MRI images
Aamir, M., Rahman, Z., Dayo, Z. A., Abro, W. A., Uddin, M. I., Khan, I., Imran, A. S., Ali, Z., Ishfaq, M., Guan, Y., et al. (2022) · Computers and Electrical Engineering · doi:10.1016/j.compeleceng.2022.108105 · Cited by 334
Kaveh, M., & Mesgari, M. S. (2023) · Neural Processing Letters · doi:10.1007/s11063-022-11055-6 · Cited by 292
Akter, A., Nosheen, N., Ahmed, S., Hossain, M., Yousuf, M. A., Almoyad, M. A. A., Hasan, K. F., & Moni, M. A. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122347 · Cited by 264
Enhancing brain tumor detection in MRI images using YOLO-NeuroBoost model
Chen, A., Lin, D., & Gao, Q. (2024) · Frontiers in Neurology · doi:10.3389/fneur.2024.1445882 · Cited by 35
Metadata
- Authors
- Irmak, E.
- Venue
- Iranian Journal of Science and Technology, Transactions of Electrical Engineering
- Published
- 2021-04-22
- Volume
- 45
- Issue
- 3
- Pages
- 1015-1036
- ISSN
- 2228-6179 (ISSN-L)
- 2364-1827
- OpenAlex
- W3159670968
- Cited by
- 477
- References
- 45
- FWCI
- 26.43
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Irmak, E. (2021). Multi-Classification of Brain Tumor MRI Images Using Deep Convolutional Neural Network with Fully Optimized Framework. Iranian Journal of Science and Technology, Transactions of Electrical Engineering. https://doi.org/10.1007/s40998-021-00426-9
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