The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Brain Tumor Detection Based on Multimodal Information Fusion and Convolutional Neural Network
Open accessAbstract
Aiming at the problem of low accuracy of traditional brain tumor detection, in this paper, a combination of multimodal information fusion and convolution neural network detection method of brain tumors, we call it a Multi-CNNs. First, this paper uses the extension of the 2D-CNNs to multimodal 3D-CNNs, and can obtain brain lesions under different modal characteristics of three-dimensional space. It can solve the 2D-CNNs raw input requires large neighborhood of faults, at the same time better to extract the modal of the differences between information. Then the real normalization layer is added between the convolution layers and pooling layer to improve the convergence speeds of the network and alleviate the problem of overfitting. In the end, the loss function was improved, and the weighted loss function was used to enhance the feature learning of the lesion area. The experimental results showed that the brain tumor detection method proposed in this paper could effectively locate tumor lesions, and better results were obtained in correlation coefficient, sensitivity, and specificity. Compared with two-dimensional detection network and single mode brain tumor detection methods, the detection accuracy is significantly improved.
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“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 [43]
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“The model was finalized after the analysis of best-performing methods existing in the literature, such as [43,52,53,58,66].”
From A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis (2022) · cites this paper as [43]
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Backpropagation Applied to Handwritten Zip Code Recognition
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The multimodal brain tumor image segmentation benchmark (BRATS)
Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2377694 · Cited by 6,781
Brain tumor segmentation with Deep Neural Networks
Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P.-M., & Larochelle, H. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.05.004 · Cited by 3,278
Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images
Pereira, S., Pinto, A., Alves, V., & Silva, C. A. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2538465 · Cited by 2,735
A deep learning model integrating FCNNs and CRFs for brain tumor segmentation
Zhao, X., Wu, Y., Song, G., Li, Z., Zhang, Y., & Fan, Y. (2018) · Medical Image Analysis · doi:10.1016/j.media.2017.10.002 · Cited by 813
Automatic segmentation of non-enhancing brain tumors in magnetic resonance images
Fletcher-Heath, L., Hall, L., Goldgof, D. B., & Murtagh, F. R. (2001) · Artificial Intelligence in Medicine · doi:10.1016/s0933-3657(00)00073-7 · Cited by 294
Neural Network Based Brain Tumor Detection Using Wireless Infrared Imaging Sensor
Mohamed Shakeel, P., Tobely, T. E. E., Al-Feel, H., Manogaran, G., & Baskar, S. (2019) · IEEE Access · doi:10.1109/access.2018.2883957 · Cited by 232
Comparison of normalization methods for the analysis of metagenomic gene abundance data
Pereira, M. B., Wallroth, M., Jönsson, V., & Kristiansson, E. (2018) · BMC Genomics · doi:10.1186/s12864-018-4637-6 · Cited by 197
Khan, M. A., Lali, M. I. U., Rehman, A. U., Ishaq, M., Sharif, M. I., Saba, T., Zahoor, S., & Akram, T. (2019) · Microscopy Research and Technique · doi:10.1002/jemt.23238 · Cited by 186
Research works printed but not linked: 1
- li, 9553, Glioma segmentation with a unified algorithm in multimodal MRI images, IEEE Access, 6
Cited by
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Brain Tumor Detection Based on Deep Learning Approaches and Magnetic Resonance Imaging
Abdusalomov, A. B., Mukhiddinov, M., & Whangbo, T. K. (2023) · Cancers · doi:10.3390/cancers15164172 · Cited by 606
Image Segmentation for MR Brain Tumor Detection Using Machine Learning: A Review
Soomro, T. A., Zheng, L., Afifi, A. J., Ali, A., Soomro, S., Yin, M., & Gao, J. (2022) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2022.3185292 · Cited by 389
Shah, H. A., Saeed, F., Yun, S., Park, J., Paul, A., & Kang, J. (2022) · IEEE Access · doi:10.1109/access.2022.3184113 · Cited by 379
Role of deep learning in brain tumor detection and classification (2015 to 2020): A review
Nazir, M., Shakil, S., & Khurshid, K. (2021) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2021.101940 · Cited by 293
A Brain Tumor Identification and Classification Using Deep Learning based on CNN-LSTM Method
Vankdothu, R., Hameed, M. A., & Fatima, H. (2022) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2022.107960 · Cited by 269
Metadata
- Authors
- Li, M., Kuang, L., Xu, S., & Sha, Z.
- Affiliations
- People’s Hospital of Rizhao (CN)
- Venue
- IEEE Access
- Published
- 2019
- Volume
- 7
- Pages
- 180134-180146
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2998623084
- Cited by
- 140
- References
- 38
- Citations by year
- 2026: 10 · 2025: 28 · 2024: 31 · 2023: 36 · 2022: 21 · 2021: 10 · 2020: 4
- FWCI
- 3.83
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- 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
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- brain tumor detection, multimodal fusion, 3D convolutional neural networks, brain lesion segmentation, weighted loss function, normalization layers, overfitting, tumor localization
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