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.

翻译：本研究探讨了通过磁共振成像（MRI）应用先进人工智能技术进行脑肿瘤分类的方法，其中训练过程整合了当前最优的深度学习模型，旨在提升诊断准确性及其在临床实践中的应用潜力。通过将定制卷积模型与预训练神经网络架构相结合，我们的方法在四分类任务（胶质瘤、脑膜瘤、垂体瘤及无肿瘤病例）中展现出卓越性能。基于包含7000余幅MRI图像的大规模数据集，我们从检测精度、计算效率及对未见数据的泛化能力三个维度对模型进行评估。结果表明，Xception架构在测试中优于所有对比模型，以最低验证损失取得了98.71%的测试准确率。在论证人工智能可作为脑肿瘤诊断有效工具的同时，我们通过降低计算复杂度为实际临床部署提供了进一步解决方案。这些研究成果为自动化神经影像诊断领域的发展开辟了广阔前景。