ResLink: A Novel Deep Learning Architecture for Brain Tumor Classification with Area Attention and Residual Connections
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914002148261888 |
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| author | Arya, Sumedha Gaud, Nirmal |
| author_facet | Arya, Sumedha Gaud, Nirmal |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_17259 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ResLink: A Novel Deep Learning Architecture for Brain Tumor Classification with Area Attention and Residual Connections Arya, Sumedha Gaud, Nirmal Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | ResLink: A Novel Deep Learning Architecture for Brain Tumor Classification with Area Attention and Residual Connections |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2508.17259 |