# CE-RS-SBCIT A Novel Channel Enhanced Hybrid CNN Transformer with Residual, Spatial, and Boundary-Aware Learning for Brain Tumor MRI Analysis

arXiv: https://arxiv.org/abs/2508.17128 · HTML: https://mlsanity.com/paper/2508.17128/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 56 references that are in the map.
- First posted on arXiv 23 Aug 2025.

## Builds on (most cited first, 20 of 20)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [MLP-Mixer: An all-MLP Architecture for Vision](https://mlsanity.com/paper/2105.01601/) (2021)
- [A Survey of the Recent Architectures of Deep Convolutional Neural Networks](https://mlsanity.com/paper/1901.06032/) (2019)
- [A Recent Survey of Vision Transformers for Medical Image Segmentation](https://mlsanity.com/paper/2312.00634/) (2023)
- [COVID-19 Detection in Chest X-Ray Images using a New Channel Boosted CNN](https://mlsanity.com/paper/2012.05073/) (2020)
- [Segmentation of Shoulder Muscle MRI Using a New Region and Edge based Deep Auto-Encoder](https://mlsanity.com/paper/2108.11720/) (2021)
- [BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification](https://mlsanity.com/paper/2506.14318/) (2025)
- [A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicron](https://mlsanity.com/paper/2202.06372/) (2022)
- [COVID-19 Detection and Analysis From Lung CT Images using Novel Channel Boosted CNNs](https://mlsanity.com/paper/2209.10963/) (2022)
- [Classification and Region Analysis of COVID-19 Infection using Lung CT Images and Deep Convolutional Neural Networks](https://mlsanity.com/paper/2009.08864/) (2020)
- [A New Deep Hybrid Boosted and Ensemble Learning-based Brain Tumor Analysis using MRI](https://mlsanity.com/paper/2201.05373/) (2022)
- [IoT Malware Detection Architecture using a Novel Channel Boosted and Squeezed CNN](https://mlsanity.com/paper/2202.04121/) (2022)
- [A New Deep Boosted CNN and Ensemble Learning based IoT Malware Detection](https://mlsanity.com/paper/2212.08008/) (2022)
- [A Comprehensive Survey on Architectural Advances in Deep CNNs: Challenges, Applications, and Emerging Research Directions](https://mlsanity.com/paper/2503.16546/) (2025)
- [Malware Classification Using Deep Boosted Learning](https://mlsanity.com/paper/2107.04008/) (2021)
- [A Novel Channel Boosted Residual CNN-Transformer with Regional-Boundary Learning for Breast Cancer Detection](https://mlsanity.com/paper/2503.15008/) (2025)
- [A Novel Feature Map Enhancement Technique Integrating Residual CNN and Transformer for Alzheimer Diseases Diagnosis](https://mlsanity.com/paper/2405.12986/) (2024)

## Built on by (most cited first, 3 of 3)

- [Residual-SwinCA-Net: A Channel-Aware Integrated Residual CNN-Swin Transformer for Malignant Lesion Segmentation in BUSI](https://mlsanity.com/paper/2512.08243/) (2025)
- [RSwinV2-MD: An Enhanced Residual SwinV2 Transformer for Monkeypox Detection from Skin Images](https://mlsanity.com/paper/2601.01835/) (2026)
- [TFT-ACB-XML: Decision-Level Integration of Customized Temporal Fusion Transformer and Attention-BiLSTM with XGBoost Meta-Learner for BTC Price Forecasting](https://mlsanity.com/paper/2602.12380/) (2026)

## Abstract (arXiv)

Brain tumors remain among the most lethal human diseases, where early detection and accurate classification are critical for effective diagnosis and treatment planning. Although deep learning-based computer-aided diagnostic (CADx) systems have shown remarkable progress. However, conventional convolutional neural networks (CNNs) and Transformers face persistent challenges, including high computational cost, sensitivity to minor contrast variations, structural heterogeneity, and texture inconsistencies in MRI data. Therefore, a novel hybrid framework, CE-RS-SBCIT, is introduced, integrating residual and spatial learning-based CNNs with transformer-driven modules. The proposed framework exploits local fine-grained and global contextual cues through four core innovations: (i) a smoothing and boundary-based CNN-integrated Transformer (SBCIT), (ii) tailored residual and spatial learning CNNs, (iii) a channel enhancement (CE) strategy, and (iv) a novel spatial attention mechanism. The developed SBCIT employs stem convolution and contextual interaction transformer blocks with systematic smoothing and boundary operations, enabling efficient global feature modeling. Moreover, Residual and spatial CNNs, enhanced by auxiliary transfer-learned feature maps, enrich the representation space, while the CE module amplifies discriminative channels and mitigates redundancy. Furthermore, the spatial attention mechanism selectively emphasizes subtle contrast and textural variations across tumor classes. Extensive evaluation on challenging MRI datasets from Kaggle and Figshare, encompassing glioma, meningioma, pituitary tumors, and healthy controls, demonstrates superior performance, achieving 98.30% accuracy, 98.08% sensitivity, 98.25% F1-score, and 98.43% precision.