Cited by 140Brain Tumor Detection and ClassificationAI in cancer detectionMedical Image Segmentation Techniques

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Brain Tumor Detection Based on Multimodal Information Fusion and Convolutional Neural Network

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Li, Ming, Kuang, Lishan, Xu, Shuhua, Sha, Zhanguo·2019·IEEE Access

DOI 10.1109/access.2019.2958370

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Abstract

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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Authors: Li, M., Kuang, L., Xu, S., & Sha, Z.
Affiliations: People’s Hospital of Rizhao (CN)
ORCID: Li, Ming
Kuang, Lishan
Xu, Shuhua
Sha, Zhanguo
Venue: IEEE Access
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2019
Volume: 7
Pages: 180134-180146
ISSN: 2169-3536 (ISSN-L)
DOI: 10.1109/access.2019.2958370
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
Licence: cc-by
creativecommons.org/licenses/by/4.0/legalcode (vor)
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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Brain Tumor Detection Based on Multimodal Information Fusion and Convolutional Neural Network

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