用AI提升脑肿瘤MRI分类准确率，达98.71%且适合临床落地。
Intelligent Systems in Neuroimaging: Pioneering AI Techniques for Brain Tumor Detection
- 融合自定义卷积与预训练模型，提升多类脑肿瘤识别性能。
- 在超7000张MRI上测试，Xception模型准确率达98.71%，验证损失最低。
- 降低计算复杂度，推动AI系统向真实医疗场景部署迈进。
本研究探讨先进AI技术在脑肿瘤MRI分类中的应用，通过引入当前最优深度学习模型以提升诊断准确率并增强临床可用性。结合定制卷积模型与预训练神经网络架构，该方法在四类分类任务（胶质瘤、脑膜瘤、垂体瘤及无肿瘤）中表现最佳。基于超过7,000张MRI图像的大规模数据集评估了模型的检测准确率、计算效率和泛化能力。结果表明，Xception架构优于所有对比模型，测试准确率达到98.71%，验证损失最低。研究展示了AI作为脑肿瘤诊断辅助工具的潜力，并通过降低计算复杂度，进一步推动其在真实临床环境中的部署可行性。该工作为自动化神经影像诊断的发展提供了广阔前景。
原文摘要 · Abstract (English)
This study deliberates on the application of advanced AI techniques for brain tumor classification through MRI, wherein the training includes the present best deep learning models to enhance diagnosis accuracy and the potential of usability in clinical practice. By combining custom convolutional models with pre-trained neural network architectures, our approach exposes the utmost performance in the classification of four classes: glioma, meningioma, pituitary tumors, and no-tumor cases. Assessing the models on a large dataset of over 7,000 MRI images focused on detection accuracy, computational efficiency, and generalization to unseen data. The results indicate that the Xception architecture surpasses all other were tested, obtaining a testing accuracy of 98.71% with the least validation loss. While presenting this case with findings that demonstrate AI as a probable scorer in brain tumor diagnosis, we demonstrate further motivation by reducing computational complexity toward real-world clinical deployment. These aspirations offer an abundant future for progress in automated neuroimaging diagnostics.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。