通过多模态神经网络检测脑肿瘤
Detecting Brain Tumors through Multimodal Neural Networks
AI总结：
本研究评估多模态神经网络对灰度MRI图像进行脑肿瘤分类的性能，模型准确率达约98%，并强调可解释性与透明性以确保人工控制和安全。
摘要：
肿瘤可以以多种形式出现在人体的不同部位。脑肿瘤因其发生的器官结构复杂，特别难以诊断和治疗。及时发现脑肿瘤可以降低死亡风险，并有助于患者的治疗过程。人工智能（AI）的应用，更具体地说是深度学习的应用，有潜力显著降低通过影像技术获取的图像中肿瘤发现和识别所需的时间和资源成本。本研究旨在评估一种多模态模型对灰度图像处理的磁共振成像（MRI）扫描进行分类的性能。结果令人鼓舞，且与类似研究一致，该模型的准确率达到了约98%。我们还强调了可解释性和透明性的必要性，以确保人工控制和安全性。
英文摘要：
Tumors can manifest in various forms and in different areas of the human body. Brain tumors are specifically hard to diagnose and treat because of the complexity of the organ in which they develop. Detecting them in time can lower the chances of death and facilitate the therapy process for patients. The use of Artificial Intelligence (AI) and, more specifically, deep learning, has the potential to significantly reduce costs in terms of time and resources for the discovery and identification of tumors from images obtained through imaging techniques. This research work aims to assess the performance of a multimodal model for the classification of Magnetic Resonance Imaging (MRI) scans processed as grayscale images. The results are promising, and in line with similar works, as the model reaches an accuracy of around 98\%. We also highlight the need for explainability and transparency to ensure human control and safety.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
