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.

翻译：肿瘤可以以多种形式出现在人体不同部位。脑肿瘤因其发生器官的复杂性而特别难以诊断和治疗。及时检测能够降低患者的死亡风险并简化治疗过程。利用人工智能（AI），特别是深度学习技术，有望显著降低通过医学成像技术获取的图像中肿瘤检测与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在处理灰度化磁共振成像（MRI）扫描图像分类任务时的性能。实验结果令人鼓舞，与同类研究相当，模型准确率达到约98%。同时，我们强调可解释性与透明度的必要性，以确保人工控制与安全性。