Precise identification of medulloblastoma in MRI images using a convolutional neural network integrated with a self-attention mechanism
Objective Medulloblastoma (MB) is a highly malignant brain tumor. Early diagnosis and treatment are important to improve patients’ survival. However, it is difficult to distinguish MB from other brain tumors in magnetic resonance imaging (MRI) with the naked eye. This study proposed a new hybrid dee...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2025-07-01
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| Series: | Digital Health |
| Online Access: | https://doi.org/10.1177/20552076251351536 |
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| _version_ | 1850060403982204928 |
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| author | Chenhao Fang Chao Li Huiqing Liu Qiang Zhou Shuo Li Hong Chen Xianzhen Chen Zhaoli Shen |
| author_facet | Chenhao Fang Chao Li Huiqing Liu Qiang Zhou Shuo Li Hong Chen Xianzhen Chen Zhaoli Shen |
| author_sort | Chenhao Fang |
| collection | DOAJ |
| description | Objective Medulloblastoma (MB) is a highly malignant brain tumor. Early diagnosis and treatment are important to improve patients’ survival. However, it is difficult to distinguish MB from other brain tumors in magnetic resonance imaging (MRI) with the naked eye. This study proposed a new hybrid deep learning model named InceptentionNet, combining Inception and self-attention mechanisms to recognize MB with MRI images. Methods InceptentionNet integrated multiscale feature extraction and dynamic focus on relevant regions. This model was trained using a dataset with 736 MRI images, including 106 MB and 630 non-MB images. Other single convolutional neural network models, including MobileNet, Residual Network, Densely Connected Convolutional Network, Visual Geometry Group, and Inception, were also trained. All models’ performance was evaluated. In addition, we conducted external tests to verify the generalization of the model. Results The InceptentionNet model achieved an accuracy of 98.07% ± 0.77%, a precision of 91.43% ± 4.56%, a F1-score of 93.54% ± 2.44%. And the area under curve and recall were respectively 99.41% ± 0.08% and 96.03% ± 3.61%. In external tests, this model still performed best, achieving 90.94% accuracy and 92.79% AUC. These metrics indicated that our model exhibited a good performance in distinguishing MB. The accuracy of InceptentionNet was the highest among other single models, indicating our hybrid model outperform other models. Additionally, images combined with attention heatmaps exhibited high clinical interpretability. Conclusion InceptentionNet demonstrates robust predictive capabilities and has the potential as a diagnostic assistant tool. In the future, the model should be trained and validated using larger data and multiclass classification should be expanded. |
| format | Article |
| id | doaj-art-431b9e44bc984f66a5c3e1f4e9afa825 |
| institution | DOAJ |
| issn | 2055-2076 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Digital Health |
| spelling | doaj-art-431b9e44bc984f66a5c3e1f4e9afa8252025-08-20T02:50:33ZengSAGE PublishingDigital Health2055-20762025-07-011110.1177/20552076251351536Precise identification of medulloblastoma in MRI images using a convolutional neural network integrated with a self-attention mechanismChenhao Fang0Chao Li1Huiqing Liu2Qiang Zhou3Shuo Li4Hong Chen5Xianzhen Chen6Zhaoli Shen7 Department of Neurosurgery, Shanghai Tenth People's Hospital, School of Medicine, , Shanghai, China Department of Neurosurgery, Shanghai Tenth People's Hospital, School of Medicine, , Shanghai, China Department of Neurosurgery, Shanghai Children's Medical Center, , Shanghai, China Department of Neurosurgery, Shanghai Tenth People's Hospital, School of Medicine, , Shanghai, China Department of Neurosurgery, Shanghai Tenth People's Hospital, School of Clinical Medicine, , Nanjing, Jiangsu, China Department of Pathology, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China Department of Neurosurgery, Shanghai Tenth People's Hospital, School of Medicine, , Shanghai, China Department of Neurosurgery, Shanghai Tenth People's Hospital, School of Medicine, , Shanghai, ChinaObjective Medulloblastoma (MB) is a highly malignant brain tumor. Early diagnosis and treatment are important to improve patients’ survival. However, it is difficult to distinguish MB from other brain tumors in magnetic resonance imaging (MRI) with the naked eye. This study proposed a new hybrid deep learning model named InceptentionNet, combining Inception and self-attention mechanisms to recognize MB with MRI images. Methods InceptentionNet integrated multiscale feature extraction and dynamic focus on relevant regions. This model was trained using a dataset with 736 MRI images, including 106 MB and 630 non-MB images. Other single convolutional neural network models, including MobileNet, Residual Network, Densely Connected Convolutional Network, Visual Geometry Group, and Inception, were also trained. All models’ performance was evaluated. In addition, we conducted external tests to verify the generalization of the model. Results The InceptentionNet model achieved an accuracy of 98.07% ± 0.77%, a precision of 91.43% ± 4.56%, a F1-score of 93.54% ± 2.44%. And the area under curve and recall were respectively 99.41% ± 0.08% and 96.03% ± 3.61%. In external tests, this model still performed best, achieving 90.94% accuracy and 92.79% AUC. These metrics indicated that our model exhibited a good performance in distinguishing MB. The accuracy of InceptentionNet was the highest among other single models, indicating our hybrid model outperform other models. Additionally, images combined with attention heatmaps exhibited high clinical interpretability. Conclusion InceptentionNet demonstrates robust predictive capabilities and has the potential as a diagnostic assistant tool. In the future, the model should be trained and validated using larger data and multiclass classification should be expanded.https://doi.org/10.1177/20552076251351536 |
| spellingShingle | Chenhao Fang Chao Li Huiqing Liu Qiang Zhou Shuo Li Hong Chen Xianzhen Chen Zhaoli Shen Precise identification of medulloblastoma in MRI images using a convolutional neural network integrated with a self-attention mechanism Digital Health |
| title | Precise identification of medulloblastoma in MRI images using a convolutional neural network integrated with a self-attention mechanism |
| title_full | Precise identification of medulloblastoma in MRI images using a convolutional neural network integrated with a self-attention mechanism |
| title_fullStr | Precise identification of medulloblastoma in MRI images using a convolutional neural network integrated with a self-attention mechanism |
| title_full_unstemmed | Precise identification of medulloblastoma in MRI images using a convolutional neural network integrated with a self-attention mechanism |
| title_short | Precise identification of medulloblastoma in MRI images using a convolutional neural network integrated with a self-attention mechanism |
| title_sort | precise identification of medulloblastoma in mri images using a convolutional neural network integrated with a self attention mechanism |
| url | https://doi.org/10.1177/20552076251351536 |
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