Abstract
Early and accurate detection of brain abnormalities, such as tumors and strokes, is essential for timely intervention and improved patient outcomes. In this study, we present a deep learning-based system capable of identifying both brain tumors and strokes from MRI images, along with their respective stages. We have executed two groundbreaking strategies involving convolutional neural networks, MobileNet V2 and ResNet-50-optimized through transfer learning to classify MRI scans into five diagnostic categories. Our dataset, aggregated and augmented from various publicly available MRI sources, was carefully curated to ensure class balance and image diversity. To enhance model generalization and prevent overfitting, we applied dropout layers and extensive data augmentation. The models achieved strong performance, with training accuracy reaching 93\% and validation accuracy up to 88\%. While ResNet-50 demonstrated slightly better results, Mobile Net V2 remains a promising option for real-time diagnosis in low resource settings due to its lightweight architecture. This research offers a practical AI-driven solution for early brain abnormality detection, with potential for clinical deployment and future enhancement through larger datasets and multi modal inputs.
Abstract (translated)
早期且准确地检测大脑异常，如肿瘤和中风，对于及时干预及改善患者预后至关重要。在本研究中，我们介绍了一种基于深度学习的系统，能够从MRI图像中识别脑瘤和中风，并确定它们各自的阶段。我们执行了两项突破性策略：采用经过迁移学习优化的卷积神经网络（CNN）MobileNet V2 和 ResNet-50 来将 MRI 扫描分类为五个诊断类别。我们的数据集是从各种公开来源汇集并扩增而来的MRI图像，为了确保类平衡和图像多样性，我们精心策划了该数据集。 为了增强模型的泛化能力和防止过拟合，我们在训练过程中应用了dropout层和广泛的图像增强技术。模型在性能上表现出色，训练准确率达到了93%，验证准确率达到88%。虽然ResNet-50展示了稍好一些的结果，但轻量级架构的Mobile Net V2 在低资源环境下的实时诊断中仍然具有很大的潜力。 这项研究为早期大脑异常检测提供了实用的人工智能解决方案，并且通过更大规模的数据集和多模态输入进一步优化后，在临床部署方面具有巨大的应用前景。
URL
https://arxiv.org/abs/2506.09161
