Brain tumors are collections of abnormal cells that can develop into masses or clusters. Because they have the potential to infiltrate other tissues, they pose a risk to the patient. The main imaging technique used, MRI, may be able to identify a brain tumor with accuracy. The fast development of Deep Learning methods for use in computer vision applications has been facilitated by a vast amount of training data and improvements in model construction that offer better approximations in a supervised setting. The need for these approaches has been the main driver of this expansion. Deep learning methods have shown promise in improving the precision of brain tumor detection and classification using magnetic resonance imaging (MRI). The study on the use of deep learning techniques, especially ResNet50, for brain tumor identification is presented in this abstract. As a result, this study investigates the possibility of automating the detection procedure using deep learning techniques. In this study, I utilized five transfer learning models which are VGG16, VGG19, DenseNet121, ResNet50 and YOLO V4 where ResNet50 provide the best or highest accuracy 99.54%. The goal of the study is to guide researchers and medical professionals toward powerful brain tumor detecting systems by employing deep learning approaches by way of this evaluation and analysis.

翻译：脑肿瘤是异常细胞的集合，可能发展成肿块或团块。由于它们具有浸润其他组织的潜力，因此对患者构成风险。主要使用的成像技术（MRI）可能能够准确识别脑肿瘤。深度学习方法在计算机视觉应用中的快速发展得益于大量训练数据以及模型构建的改进，这些改进在监督环境下提供了更好的近似。对这些方法的需求是这一扩展的主要驱动力。深度学习方法已显示出在提高使用磁共振成像（MRI）进行脑肿瘤检测和分类精度方面的潜力。本摘要介绍了关于使用深度学习技术（尤其是ResNet50）进行脑肿瘤识别的研究。因此，本研究探讨了利用深度学习技术自动化检测流程的可能性。研究中我使用了五种迁移学习模型，即VGG16、VGG19、DenseNet121、ResNet50和YOLO V4，其中ResNet50提供了最佳或最高精度99.54%。本研究的目的是通过本次评估与分析，指导研究人员和医疗专业人员借助深度学习方法构建强大的脑肿瘤检测系统。