Optimization Methodology for Meningioma and Acoustic Neuroma Detection Model Based on DCGAN
Due to the extreme similarity in imaging manifestations and locations of onset between meningiomas and acoustic neuromas in the CPA (cerebellopontine angle) region of the human body, clinical diagnosis is prone to misdiagnosis. Establishing an automatic tumor detection model using deep learning meth...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Science Press
2025-06-01
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| Series: | Chinese Journal of Magnetic Resonance |
| Subjects: | |
| Online Access: | http://121.43.60.238/bpxzz/article/2025/1000-4556/1000-4556-42-2-117.shtml |
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| Summary: | Due to the extreme similarity in imaging manifestations and locations of onset between meningiomas and acoustic neuromas in the CPA (cerebellopontine angle) region of the human body, clinical diagnosis is prone to misdiagnosis. Establishing an automatic tumor detection model using deep learning methods can effectively reduce the subjectivity of manual diagnosis, decrease missed diagnosis rates, and improve work efficiency. The diversity of datasets and superiority of image quality largely determine the performance of the detection model. This paper proposes a DCGAN (deep convolutional generative adversarial networks) with improved loss function for data augmentation of meningioma and acoustic neuroma detection models to address the issues of scarce medical image datasets, imbalanced number of categories, and poor imaging quality. Compared with traditional dataset augmentation methods, the results show that after optimizing the dataset with DCGAN, the accuracy, specificity, and mAP (mean average precision) of the brain tumor detection model increase by 0.014 6, 0.022 4, and 0.030 0 respectively compared to the original dataset, reaching 0.932 8, 0.898 6, and 0.930 0. The study demonstrates that optimizing datasets with DCGAN can significantly improve the performance of the brain tumor detection model, providing a more reliable tool for clinical medical diagnosis. |
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| ISSN: | 1000-4556 |