SERA-Net: Rethinking CNN Design for Brain Tumor Classification via Squeeze-and-Excite Attention and Residual Learning

A brain tumor is a life-threatening disease that carries a high mortality burden. It can be treated if detected in the early stages. Manual analysis by a radiologist using Magnetic Resonance Imaging (MRI) is effective but slow and subject to inter-observer variability. In order to automate it, various deep learning models, especially Convolutional Neural Networks (CNNs), have been widely used. However, most of them rely on frozen, ImageNet-based pretrained backbones, where only a small classification head is updated during training. This leaves a large segment of parameters unable to adapt to the domain shift between natural images and MRI scans. This limits how far such models can be modified or improved further. This makes the model highly parameter-centric and unsuitable for development. In addition, most studies fail to provide insights on architectural choices, which inhibits reproduction and modifications. In order to alleviate these issues, this study presents SERA-Net, a convolutional architecture for brain tumor classification using MRI scans. It is trained from scratch and quantifies the performance of each model using an incremental ablation study. It consists of four blocks comprising different components, such as Squeeze-and-Excite channel attention, residual connections, a wider classification head, etc. Rather than proposing new operations, the contribution lies in the systematic, ablation-driven combination and empirical validation of established architectural components for this specific task. The model was trained and evaluated on a publicly available MRI dataset. With just ≈5 million parameters, it achieved an accuracy of 95.44%, an F1-score of 0.953, and various near-ideal parameters. It was observed to outperform six pretrained CNN baselines by more than 5–10%, while using substantially fewer parameters. Various other analyses indicate the efficacy of the proposed approach in detecting brain tumors. The proposed approach provides a scalable design that can be adapted to different domains with limited modifications.