# ResLink: A Novel Deep Learning Architecture for Brain Tumor Classification with Area Attention and Residual Connections

arXiv: https://arxiv.org/abs/2508.17259 · HTML: https://mlsanity.com/paper/2508.17259/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 22 references that are in the map.
- First posted on arXiv 24 Aug 2025.

## Builds on (most cited first, 14 of 14)

- [A Deep Learning Approach for Brain Tumor Classification and Segmentation Using a Multiscale Convolutional Neural Network](https://mlsanity.com/paper/2402.05975/) (2024)
- [Deep Learning in Medical Image Classification from MRI-based Brain Tumor Images](https://mlsanity.com/paper/2408.00636/) (2024)
- [Brain Tumor Classification on MRI in Light of Molecular Markers](https://mlsanity.com/paper/2409.19583/) (2024)
- [An Ensemble Approach for Brain Tumor Segmentation and Synthesis](https://mlsanity.com/paper/2411.17617/) (2024)
- [Brain Tumor Classification using Vision Transformer with Selective Cross-Attention Mechanism and Feature Calibration](https://mlsanity.com/paper/2406.17670/) (2024)
- [Comparative Analysis of Resource-Efficient CNN Architectures for Brain Tumor Classification](https://mlsanity.com/paper/2411.15596/) (2024)
- [Efficient Feature Extraction and Classification Architecture for MRI-Based Brain Tumor Detection and Localization](https://mlsanity.com/paper/2410.22619/) (2024)
- [Residual Vision Transformer (ResViT) Based Self-Supervised Learning Model for Brain Tumor Classification](https://mlsanity.com/paper/2411.12874/) (2024)
- [Brain Tumor Classification From MRI Images Using Machine Learning](https://mlsanity.com/paper/2407.10630/) (2024)
- [Machine learning approach to brain tumor detection and classification](https://mlsanity.com/paper/2410.12692/) (2024)
- [Tumor Location-weighted MRI-Report Contrastive Learning: A Framework for Improving the Explainability of Pediatric Brain Tumor Diagnosis](https://mlsanity.com/paper/2411.00609/) (2024)
- [Enhancing Brain Tumor Classification Using TrAdaBoost and Multi-Classifier Deep Learning Approaches](https://mlsanity.com/paper/2411.00875/) (2024)
- [Efficient Brain Tumor Classification with Lightweight CNN Architecture: A Novel Approach](https://mlsanity.com/paper/2502.01674/) (2025)
- [A CNN Approach to Automated Detection and Classification of Brain Tumors](https://mlsanity.com/paper/2502.09731/) (2025)

## Abstract (arXiv)

Brain tumors show significant health challenges due to their potential to cause critical neurological functions. Early and accurate diagnosis is crucial for effective treatment. In this research, we propose ResLink, a novel deep learning architecture for brain tumor classification using CT scan images. ResLink integrates novel area attention mechanisms with residual connections to enhance feature learning and spatial understanding for spatially rich image classification tasks. The model employs a multi-stage convolutional pipeline, incorporating dropout, regularization, and downsampling, followed by a final attention-based refinement for classification. Trained on a balanced dataset, ResLink achieves a high accuracy of 95% and demonstrates strong generalizability. This research demonstrates the potential of ResLink in improving brain tumor classification, offering a robust and efficient technique for medical imaging applications.