Brain tumors are one of the most common diseases that lead to early death if not diagnosed at an early stage. Traditional diagnostic approaches are extremely time-consuming and prone to errors. In this context, computer vision-based approaches have emerged as an effective tool for accurate brain tumor classification. While some of the existing solutions demonstrate noteworthy accuracy, the models become infeasible to deploy in areas where computational resources are limited. This research addresses the need for accurate and fast classification of brain tumors with a priority of deploying the model in technologically underdeveloped regions. The research presents a novel architecture for precise brain tumor classification fusing pretrained ResNet152V2 and modified VGG16 models. The proposed architecture undergoes a diligent fine-tuning process that ensures fine gradients are preserved in deep neural networks, which are essential for effective brain tumor classification. The proposed solution incorporates various image processing techniques to improve image quality and achieves an astounding accuracy of 98.36% and 98.04% in Figshare and Kaggle datasets respectively. This architecture stands out for having a streamlined profile, with only 2.8 million trainable parameters. We have leveraged 8-bit quantization to produce a model of size 73.881 MB, significantly reducing it from the previous size of 289.45 MB, ensuring smooth deployment in edge devices even in resource-constrained areas. Additionally, the use of Grad-CAM improves the interpretability of the model, offering insightful information regarding its decision-making process. Owing to its high discriminative ability, this model can be a reliable option for accurate brain tumor classification.

翻译：脑肿瘤是最常见的疾病之一，若未在早期诊断将导致过早死亡。传统诊断方法极为耗时且易出错。在此背景下，基于计算机视觉的方法已成为实现精确脑肿瘤分类的有效工具。尽管现有部分解决方案展现出显著准确度，但这些模型在计算资源有限的地区难以部署。本研究针对脑肿瘤精确快速分类的需求，优先考虑在技术欠发达区域部署模型。研究提出了一种融合预训练ResNet152V2与改进VGG16模型的新型架构，用于实现精确脑肿瘤分类。该架构经过精细的微调过程，确保深度神经网络中保留对脑肿瘤分类至关重要的细粒度梯度。所提方案结合多种图像处理技术以提升图像质量，在Figshare和Kaggle数据集上分别达到98.36%和98.04%的惊人准确率。该架构具有精简的参数配置，仅含280万个可训练参数。我们采用8位量化技术将模型大小从原有的289.45 MB显著压缩至73.881 MB，确保即使在资源受限区域的边缘设备上也能顺畅部署。此外，Grad-CAM技术的运用提升了模型的可解释性，为其决策过程提供了深入洞察。凭借其卓越的判别能力，该模型可成为脑肿瘤精确分类的可靠选择。