  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.
