脑肿瘤的早期检测对于全球的临床医生来说变得越来越复杂。脑肿瘤患者的诊断在后期是严格的，这是一个严重的问题。虽然有相关的实用临床工具和基于机器学习（ML）的多个模型用于有效诊断患者，但这些模型仍然提供较低的准确性，并且在诊断过程中对患者筛选需要耗费大量时间。因此，仍然需要开发一种更精确的模型，以更准确地筛选患者，早期发现脑肿瘤，并帮助临床医生诊断，使脑肿瘤评估更可靠。在这项研究中，我们提出了不同生成对抗网络（GAN）对早期脑肿瘤检测影响的性能分析。基于此，我们提出了一种新的混合增强预测卷积神经网络（CNN）模型，使用混合GAN集成。利用GAN集成来增强脑肿瘤图像数据，并使用混合调制CNN技术进行分类。通过软投票方法生成结果，最终预测基于计算不同性能指标的GAN中的最高值。分析表明，在渐进生成对抗网络（PGGAN）架构的评估中获得了最好的结果。在该分析中，PGGAN在准确率、精确度、召回率、F1分数和阴性预测值（NPV）方面表现出色，分别为98.85％，98.45％，97.2％，98.11％和98.09％。此外，PGGAN的延迟非常低，为3.4秒。PGGAN模型提高了实时识别脑细胞组织的整体性能。因此，可以推断出使用PGGAN增强和提出的调制CNN技术进行患者脑肿瘤检测的最佳性能是使用软投票方法的。

Brain tumor detection in the initial stage is becoming an intricate task for clinicians worldwide. The diagnosis of brain tumor patients is rigorous in the later stages, which is a serious concern. Although there are related pragmatic clinical tools and multiple models based on machine learning (ML) for the effective diagnosis of patients, these models still provide less accuracy and take immense time for patient screening during the diagnosis process. Hence, there is still a need to develop a more precise model for more accurate screening of patients to detect brain tumors in the beginning stages and aid clinicians in diagnosis, making the brain tumor assessment more reliable. In this research, a performance analysis of the impact of different generative adversarial networks (GAN) on the early detection of brain tumors is presented. Based on it, a novel hybrid enhanced predictive convolution neural network (CNN) model using a hybrid GAN ensemble is proposed. Brain tumor image data is augmented using a GAN ensemble, which is fed for classification using a hybrid modulated CNN technique. The outcome is generated through a soft voting approach where the final prediction is based on the GAN, which computes the highest value for different performance metrics. This analysis demonstrated that evaluation with a progressive-growing generative adversarial network (PGGAN) architecture produced the best result. In the analysis, PGGAN outperformed others, computing the accuracy, precision, recall, F1-score, and negative predictive value (NPV) to be 98.85, 98.45%, 97.2%, 98.11%, and 98.09%, respectively. Additionally, a very low latency of 3.4 s is determined with PGGAN. The PGGAN model enhanced the overall performance of the identification of brain cell tissues in real time. Therefore, it may be inferred to suggest that brain tumor detection in patients using PGGAN augmentation with the proposed modulated CNN technique generates the optimum performance using the soft voting approach.