Dendritic Aggregated Residual Deep Learning for Meningioma MRI Diagnosis
The diagnosis of meningioma through magnetic resonance imaging (MRI) holds significant importance in clinical medicine. To enhance the accuracy of meningioma diagnosis, a more effective image classification method is required to comprehensively capture subtle features within MRI images. Although dee...
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| Published in | IEICE Transactions on Information and Systems Vol. E108.D; no. 8; pp. 1016 - 1019 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
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The Institute of Electronics, Information and Communication Engineers
01.08.2025
一般社団法人 電子情報通信学会 |
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| ISSN | 0916-8532 1745-1361 |
| DOI | 10.1587/transinf.2024EDL8049 |
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| Abstract | The diagnosis of meningioma through magnetic resonance imaging (MRI) holds significant importance in clinical medicine. To enhance the accuracy of meningioma diagnosis, a more effective image classification method is required to comprehensively capture subtle features within MRI images. Although deep learning networks have been successfully applied to this problem, the conventional neural networks based on McCulloch-Pitts neurons suffer from low performance and insufficient feature extraction, due to simplistic structure and neglect of the nonlinear effects of synapses. Therefore, we propose a novel dendritic learning-based ResNeXt model, named DResNeXt. It utilizes the residual structure of ResNeXt and the cardinality method to adequately extract features of MRI images. Then, we innovationally introduce a dendritic neural model to improve the nonlinear information processing of biological neurons for comprehensively handling extracted features. Experimental results demonstrate the outstanding performance of the proposed DResNeXt model in the classification task of meningioma MRI dataset, surpassing the ResNeXt model in preventing overfitting. Additionally, compared to other deep learning models, it exhibits higher accuracy and superior image classification performance. |
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| AbstractList | The diagnosis of meningioma through magnetic resonance imaging (MRI) holds significant importance in clinical medicine. To enhance the accuracy of meningioma diagnosis, a more effective image classification method is required to comprehensively capture subtle features within MRI images. Although deep learning networks have been successfully applied to this problem, the conventional neural networks based on McCulloch-Pitts neurons suffer from low performance and insufficient feature extraction, due to simplistic structure and neglect of the nonlinear effects of synapses. Therefore, we propose a novel dendritic learning-based ResNeXt model, named DResNeXt. It utilizes the residual structure of ResNeXt and the cardinality method to adequately extract features of MRI images. Then, we innovationally introduce a dendritic neural model to improve the nonlinear information processing of biological neurons for comprehensively handling extracted features. Experimental results demonstrate the outstanding performance of the proposed DResNeXt model in the classification task of meningioma MRI dataset, surpassing the ResNeXt model in preventing overfitting. Additionally, compared to other deep learning models, it exhibits higher accuracy and superior image classification performance. |
| ArticleNumber | 2024EDL8049 |
| Author | Ningning WANG Rong-Long WANG Qianhang DU Zijing YUAN Yu GAO Shangce GAO |
| Author_xml | – sequence: 1 givenname: Ningning surname: WANG fullname: WANG, Ningning – sequence: 2 givenname: Qianhang surname: DU fullname: DU, Qianhang – sequence: 3 givenname: Zijing surname: YUAN fullname: YUAN, Zijing – sequence: 4 givenname: Yu surname: GAO fullname: GAO, Yu – sequence: 5 givenname: Rong-Long surname: WANG fullname: WANG, Rong-Long – sequence: 6 givenname: Shangce surname: GAO fullname: GAO, Shangce |
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| Cites_doi | 10.1007/s40998-021-00426-9 10.1007/s10278-020-00371-9 10.1016/j.procs.2021.01.025 10.1109/JAS.2023.123978 10.1109/TIM.2023.3290301 10.1109/ACCESS.2020.3005450 10.1109/TNNLS.2021.3105901 10.1109/TNNLS.2018.2846646 10.1109/TIFS.2023.3328431 10.1016/j.engstruct.2022.115406 10.1109/JAS.2023.123813 10.1109/JBHI.2021.3100758 10.1007/BF02478259 10.1109/CVPR.2017.243 |
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| References_xml | – reference: [7] G. Huang, Z. Liu, L. van der Maaten, and K.Q. Weinberger, “Densely connected convolutional networks,” Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.2261-2269, July 2017. 10.1109/cvpr.2017.243 – reference: [10] S. Gao, M. Zhou, Z. Wang, D. Sugiyama, J. Cheng, J. Wang, and Y. Todo, “Fully complex-valued dendritic neuron model,” IEEE Trans. Neural Netw. Learn. Syst., vol.34, no.4, pp.2105-2118, 2023. 10.1109/tnnls.2021.3105901 – reference: [14] L. Li, Y. Zhu, and Z. Zhu, “Automatic modulation classification using ResNeXt-GRU with deep feature fusion,” IEEE Trans. Instrum. Meas., vol.72, pp.1-10, Art no. 2519710, 2023. 10.1109/tim.2023.3290301 – reference: [6] H. Pan, Z. Pang, Y. Wang, Y. Wang, and L. Chen, “A new image recognition and classification method combining transfer learning algorithm and MobileNet model for welding defects,” IEEE Access, vol.8, pp.119951-119960, 2020. 10.1109/access.2020.3005450 – reference: [13] Z. Liu, Z. Zhang, Z. Lei, M. Omura, R.-L. Wang, and S. Gao, “Dendritic deep learning for medical segmentation,” IEEE/CAA J. Autom. Sin., vol.11, no.3, pp.803-805, 2024. 10.1109/jas.2023.123813 – reference: [11] S. Gao, M. Zhou, Y. Wang, J. Cheng, H. Yachi, and J. Wang, “Dendritic neuron model with effective learning algorithms for classification, approximation, and prediction,” IEEE Trans. Neural Netw. Learn. Syst., vol.30, no.2, pp.601-614, 2019. 10.1109/tnnls.2018.2846646 – reference: [1] E. Irmak, “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, vol.45, no.3, pp.1015-1036, 2021. 10.1007/s40998-021-00426-9 – reference: [2] K.M. Hosny, M.A. Kassem, and M.M. Fouad, “Classification of skin lesions into seven classes using transfer learning with AlexNet,” Journal of Digital Imaging, vol.33, pp.1325-1334, 2020. 10.1007/s10278-020-00371-9 – reference: [12] Z. Zhang, Z. Lei, M. Omura, H. Hasegawa, and S. Gao, “Dendritic learning-incorporated vision transformer for image recognition,” IEEE/CAA J. Autom. Sin., vol.11, no.2, pp.539-541, 2024. 10.1109/jas.2023.123978 – reference: [9] W.S. McCulloch and W. Pitts, “A logical calculus of the ideas immanent in nervous activity,” The Bulletin of Mathematical Biophysics, vol.5, pp.115-133, 1943. 10.1007/bf02478259 – reference: [4] A. Sekhar, S. Biswas, R. Hazra, A.K. Sunaniya, A. Mukherjee, and L. Yang, “Brain tumor classification using fine-tuned GoogleNet features and machine learning algorithms: IoMT enabled CAD system,” IEEE J. Biomed. Health Inform., vol.26, no.3, pp.983-991, 2021. 10.1109/jbhi.2021.3100758 – reference: [5] D. Sarwinda, R.H. Paradisa, A. Bustamam, and P. Anggia, “Deep learning in image classification using residual network (ResNet) variants for detection of colorectal cancer,” Procedia Computer Science, vol.179, pp.423-431, 2021. 10.1016/j.procs.2021.01.025 – reference: [3] Y. Que, Y. Dai, X. Ji, A.K. Leung, Z. Chen, Z. Jiang, and Y. Tang, “Automatic classification of asphalt pavement cracks using a novel integrated generative adversarial networks and improved VGG model,” Engineering Structures, vol.277, 115406, 2023. 10.1016/j.engstruct.2022.115406 – reference: [8] Y. He, X. Kang, Q. Yan, and E. Li, “ResNeXt+: Attention mechanisms based on ResNeXt for malware detection and classification,” IEEE Trans. Inf. Forensics Secur., vol.19, pp.1142-1155, 2024. 10.1109/tifs.2023.3328431 – ident: 1 doi: 10.1007/s40998-021-00426-9 – ident: 2 doi: 10.1007/s10278-020-00371-9 – ident: 5 doi: 10.1016/j.procs.2021.01.025 – ident: 12 doi: 10.1109/JAS.2023.123978 – ident: 14 doi: 10.1109/TIM.2023.3290301 – ident: 6 doi: 10.1109/ACCESS.2020.3005450 – ident: 10 doi: 10.1109/TNNLS.2021.3105901 – ident: 11 doi: 10.1109/TNNLS.2018.2846646 – ident: 8 doi: 10.1109/TIFS.2023.3328431 – ident: 3 doi: 10.1016/j.engstruct.2022.115406 – ident: 13 doi: 10.1109/JAS.2023.123813 – ident: 4 doi: 10.1109/JBHI.2021.3100758 – ident: 9 doi: 10.1007/BF02478259 – ident: 7 doi: 10.1109/CVPR.2017.243 |
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| Title | Dendritic Aggregated Residual Deep Learning for Meningioma MRI Diagnosis |
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