Brain Tumor Detection Based on Multimodal Information Fusion and Convolutional Neural Network
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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 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 [43]
The model for brain tumor detection was finalized after analysis of best-performing methods existing in the literature.
“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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RetractedYao, X., Wu, Y., Xie, G., Zhao, D., & Xia, D. (2025) · Corrosion Engineering Science and Technology The International Journal of Corrosion Processes and Corrosion Control · doi:10.1177/1478422x251387524 · Cited by 5 · see citing works
Brain tumour segmentation of MR images based on custom attention mechanism with transfer‐learning
Vatanpour, M., & Haddadnia, J. (2023) · IET Image Processing · doi:10.1049/ipr2.12992 · Cited by 5 · see citing works
Huang, H., Dong, L., Xue, Z., Liu, X., & Hua, C. (2021) · PLoS ONE · doi:10.1371/journal.pone.0245563 · Cited by 5 · see citing works
Hierarchical based tumor segmentation by detection using deep learning approach
Sahaai, M. B., & Jothilakshmi, G. R. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1921/1/012080 · Cited by 5 · see citing works
Tariq, U., Ahmed, I., Shaukat, K., & Ihsan, M. (2026) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-026-01283-2 · Cited by 4 · see citing works
Neethu, M. S., & Jayasingh, J. R. (2023) · Journal of Mechanics in Medicine and Biology · doi:10.1142/s0219519423500331 · Cited by 4 · see citing works
Automated evaluation and parameter estimation of brain tumor using deep learning techniques
Vijayakumari, B., Kiruthiga, N., & Bushkala, C. P. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10255-6 · Cited by 4 · see citing works
Brain Tumor Detection and Segmentation Using Deep Learning Models
Abiodun, M. K., Adeniyi, A. E., Awotunde, J. B., Aworinde, H. O., Tinuoye, G., Imoize, A. L., Li, C., & Lee, C. (2024) · World Symposium on Artificial Intelligence (WSAI) · doi:10.1109/wsai62426.2024.10829366 · Cited by 4 · see citing works
Hoseini, N. S., Kargar, M., Bayani, A., & Ardebili, S. (2025) · Computational and Systems Oncology · doi:10.1002/cso2.70002 · Cited by 4 · see citing works
Detection of Brain Tumor Types Using Deep Learning
Roopa, Y. M., Kumar, G. N., Harsha, Y. V. S., & Aditya, P. S. (2022) · International Conference on Artificial Intelligence and Smart Energy (ICAIS) · doi:10.1109/icais53314.2022.9742731 · Cited by 4 · see citing works
A Brief Survey on Feature Extraction Models for Brain Tumor Detection
Janapati, M., & Akhtar, S. (2023) · International Conference on Artificial Intelligence and Smart Energy (ICAIS) · doi:10.1109/icais56108.2023.10073722 · Cited by 4 · see citing works
Semi-supervised 3D brain tumor detection system using a tumor cut-based technique
Ananthanayaki, S., & Annadhason, A. (2023) · Soft Computing · doi:10.1007/s00500-023-07943-1 · Cited by 3 · see citing works
P, G. M., Lakshmi, K. P., Arumugam, S. R., & Sandhya, N. (2023) · The Imaging Science Journal · doi:10.1080/13682199.2023.2178611 · Cited by 3 · see citing works
MRI Image Analysis for Brain Tumor Detection Using Convolutional Neural Network
Ayshwarya, B., Dhanamalar, M., & Kumar, V. (2023) · International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) · doi:10.1109/iceconf57129.2023.10083560 · Cited by 3 · see citing works
Enhanced Brain Tumor Diagnosis through Multimodal Fusion and 3D Convolutional Neural Networks
J, C. M. B. M., Kannan, E., Alex David, S., Thangarasu, G., & Begum, A. (2023) · International Conference On Smart Technologies For Smart Nation (SmartTechCon) · doi:10.1109/smarttechcon57526.2023.10391443 · Cited by 3 · see citing works
A transfer learning based model for brain tumor detection using magnetic resonance imaging
Srivastava, S., Ahmed, T., Kumar, R., & Saxena, A. (2025) · AIP conference proceedings · doi:10.1063/5.0246542 · Cited by 3 · see citing works
Hybrid Segmentation Approach for Tumors Detection in Brain Using Machine Learning Algorithms
Praveena, M., & Rao, M. K. (2023) · International Journal of Image and Graphics · doi:10.1142/s0219467823400089 · Cited by 3 · see citing works
Meeradevi, T., Soundarya, G., Sowmiya, G., & Viknesh, A. (2024) · International Conference on Advanced Computing and Communication Systems (ICACCS) · doi:10.1109/icaccs60874.2024.10717203 · Cited by 3 · see citing works
Raju, A. S. N., Venkatesh, K., Gatla, R. K., Hussain, S. J., & Polamurı, S. R. (2025) · Cognitive Computation · doi:10.1007/s12559-025-10433-1 · Cited by 3 · see citing works
Classification Of Brain Tumor Using Generative Adversarial Network With RES NET Discriminator
Umamaheswari, M., Sivadasan, J., Dwibedi, R. K., Senthilkumar, B., Rani, L. P., & Oviya, S. (2024) · International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS) · doi:10.1109/adics58448.2024.10533523 · Cited by 2 · see citing works
Al-Rumaihi, G., Khan, M. M., Saleh, A., Ali, A., Al-Romaihi, L., Al-Jaber, N., Al-Suliaiti, G., Chowdhury, M. E. H., Babu, G. R., & Pedersen, S. (2025) · Cureus · doi:10.7759/cureus.88915 · Cited by 2 · see citing works
Ramu, B., & Bansal, S. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15480-0 · Cited by 2 · see citing works
Performance Analysis of Several CNN Based Models for Brain MRI in Tumor Classification
Wahlang, I., & Pohrmen, F. H. (2024) · IEEE International Conference on Signal and Image Processing Applications (ICSIPA) · doi:10.1109/icsipa62061.2024.10685883 · Cited by 2 · see citing works
Asiri, A. A. M., Soomro, T. A., Ali, A., Ubaid, F. B., Irfan, M., Mehdar, K. M., Alelyani, M., Alshuhri, M. S., Alghamdi, A. J., & Alamri, S. (2025) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2025.061683 · Cited by 2 · see citing works
A COMPREHENSIVE STUDY ON APPLICATION OF DEEP LEARNING IN BRAIN TUMOR DETECTION
Mathew, J., Srinivasan, N., & Adjunct Professor, B. P. (2021) · Journal of University of Shanghai for Science and Technology · doi:10.51201/jusst/21/07178 · Cited by 2 · see citing works
Brain Tumor Detection and Segmentation Using Anisotropic Filtering
Mishra, A., Singh, A., & Pal, A. (2023) · International Conference on Signal Processing and Integrated Networks (SPIN) · doi:10.1109/spin57001.2023.10116924 · Cited by 2 · see citing works
Taxonomy early detection of brain cancer approach ridge polynomial neural network
Ginting, R. U., Sihombing, P., Efendi, S., AMILA, A., & Damanik, B. (2023) · AIP conference proceedings · doi:10.1063/5.0154523 · Cited by 1 · see citing works
Multi-Modality Image Fusion Approach for Identifying Brain Tumors
Peddinti, A. S., Kalyani, K., Preethi, V., Raju, D. M., & Kumar, S. (2024) · International Conference on Computational Intelligence for Green and Sustainable Technologies (ICCIGST) · doi:10.1109/iccigst60741.2024.10717536 · Cited by 1 · see citing works
Brain tumor detection using with GW Deep CNN LSTM
Palleti, V. K., & Sivappagari, C. M. R. (2024) · International Conference on Recent Advances in Science and Engineering Technology (ICRASET) · doi:10.1109/icraset63057.2024.10894918 · Cited by 1 · see citing works
A Comprehensive Review of AI Techniques for Brain Tumor Classification and Segmentation
Lakhanpal, A., Baghela, V. S., & Tiwari, S. (2025) · International Conference on Circuit, Systems and Communication (ICCSC) · doi:10.1109/iccsc66714.2025.11135034 · Cited by 1 · see citing works
Comparing SVM and KNN Algorithm for High Accuracy in Brain Tumor Prediction
Kanimozhi, J. K., Kalasurya, G., Anitha, P., Geetha, R., Loganathan, E., & Kandasamy, C. A. (2025) · International Conference on Computing Technologies & Data Communication (ICCTDC) · doi:10.1109/icctdc64446.2025.11158881 · Cited by 1 · see citing works
Brain Tumor Detection Using Deep Learning with EfficientNet-B0
Chandana, Y., Moturi, S., Bhavani, D., Triveni, B., Lakshmi, P. N., Reddy, D. V., & Rao, S. S. N. (2025) · Smart innovation, systems and technologies · doi:10.1007/978-981-96-6250-0_41 · Cited by 1 · see citing works
Das, A., Chatterjee, S., Biswas, S., & Pandit, S. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.109306 · Cited by 1 · see citing works
Dehariya, A. K., & Shukla, P. (2021) · Studies in autonomic, data-driven and industrial computing · doi:10.1007/978-981-16-3915-9_19 · Cited by 1 · see citing works
Gaussian Weighted Deep CNN with LSTM for Brain Tumor Detection
Kumar, V., & Prince, P. G. K. (2023) · International Journal of Electrical and Electronics Engineering · doi:10.14445/23488379/ijeee-v10i1p119 · Cited by 1 · see citing works
A FEATURE-BASED DECISION MAKING APPROACH (FBDMA) FOR TUMOR DETECTION USING DEEP LEARNING
Durga, P., & Sudhakar, T. (2023) · Indian Journal of Computer Science and Engineering · doi:10.21817/indjcse/2023/v14i1/231401068 · Cited by 1 · see citing works
Brain Tumor Detection Based on Different Deep Neural Networks - A Comparison Study
Shrividhiya, G., Seshagiribabu, S. K., Kashyap, S. N., Gururaj, C., & Seshagiribabu, I. K. (2023) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9789815080230123020006 · Cited by 1 · see citing works
A Systematic Review for Medical Data Fusion Over Wireless Multimedia Sensor Networks
Anita, J. N., & Kumaran, S. (2023) · doi:10.1002/9781394175253.ch7 · Cited by 1 · see citing works
Deep Learning Model with VGG16 Model for Brain Tumour Detection
Mehan, M., -, D. H. K., & Sohi, N. (2024) · International Journal For Multidisciplinary Research · doi:10.36948/ijfmr.2024.v06i01.11612 · Cited by 1 · see citing works
Thilagavathi, S., Sridhar, D., & Sundaram, J. (2024) · Advances in medical diagnosis, treatment, and care (AMDTC) book series · doi:10.4018/979-8-3693-1281-0.ch008 · Cited by 1 · see citing works
Brain Tumor Detection Using Convolutional Neural Network and Transfer Learning Approach
Tyagi, M., & Priyanka (2023) · doi:10.1109/icacctech61146.2023.00091 · Cited by 1 · see citing works
Brain Tumor Identification using Convolutional Neural Network
Sanapala, S., Rashmi, M. R., & Özer, T. (2024) · International Conference on Smart Electronics and Communication (ICOSEC) · doi:10.1109/icosec61587.2024.10722290 · Cited by 1 · see citing works
Early Detection of Brain Tumor using ResNet50 and Transfer Learning
Desai, S., & Mushrif, M. M. (2024) · International Conference on Automation, Computing and Renewable Systems (ICACRS) · doi:10.1109/icacrs62842.2024.10841701 · Cited by 1 · see citing works
DEVELOPMENT OF A CONVOLUTIONAL NEURAL NETWORK MODEL FOR BRAIN ABNORMALITY CLASSIFICATION
Barad, H., & Patel, A. M. (2020) · Indian Journal of Computer Science and Engineering · doi:10.21817/indjcse/2020/v11i6/201106058 · Cited by 1 · see citing works
- 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
- Life Sciences › Neuroscience › Neurology › 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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