mlsanityData to 14 Oct 2026

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

arXiv 2508.17259 ↗First posted 24 Aug 2025cs.CV cs.AIcited by 0

Not on the map. Why?

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.

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

14 of 22 references.

References · 14

  1. 2024A Deep Learning Approach for Brain Tumor Classification and Segmentation Using a Multiscale Convolutional Neural Networkcited by 7
  2. 2024Deep Learning in Medical Image Classification from MRI-based Brain Tumor Imagescited by 7
  3. 2024Brain Tumor Classification on MRI in Light of Molecular Markerscited by 5
  4. 2024An Ensemble Approach for Brain Tumor Segmentation and Synthesiscited by 5
  5. 2024Brain Tumor Classification using Vision Transformer with Selective Cross-Attention Mechanism and Feature Calibrationcited by 3
  6. 2024Comparative Analysis of Resource-Efficient CNN Architectures for Brain Tumor Classificationcited by 3
  7. 2024Efficient Feature Extraction and Classification Architecture for MRI-Based Brain Tumor Detection and Localizationcited by 2
  8. 2024Residual Vision Transformer (ResViT) Based Self-Supervised Learning Model for Brain Tumor Classificationcited by 2
  9. 2024Brain Tumor Classification From MRI Images Using Machine Learningcited by 1
  10. 2024Machine learning approach to brain tumor detection and classificationcited by 1
  11. 2024Tumor Location-weighted MRI-Report Contrastive Learning: A Framework for Improving the Explainability of Pediatric Brain Tumor Diagnosiscited by 1
  12. 2024Enhancing Brain Tumor Classification Using TrAdaBoost and Multi-Classifier Deep Learning Approachescited by 1
2 more
  1. 2025Efficient Brain Tumor Classification with Lightweight CNN Architecture: A Novel Approachcited by 1
  2. 2025A CNN Approach to Automated Detection and Classification of Brain Tumorscited by 1

Built on by

Not cited yet.