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

• URL: http://arxiv.org/abs/2508.17128v2
• Date: Fri, 29 Aug 2025 04:47:15 GMT
• Title: CE-RS-SBCIT A Novel Channel Enhanced Hybrid CNN Transformer with Residual, Spatial, and Boundary-Aware Learning for Brain Tumor MRI Analysis
• Authors: Mirza Mumtaz Zahoor, Saddam Hussain Khan,
• Abstract summary: The framework exploits local fine-grained and global contextual cues through four core innovations.<n>The framework achieves 98.30% accuracy, 98.08% sensitivity, 98.25% F1-score, and 98.43% precision.
• Score: 0.7857499581522376
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

Related papers

• RS-CA-HSICT: A Residual and Spatial Channel Augmented CNN Transformer Framework for Monkeypox Detection [0.7857499581522376]
  This work proposes a hybrid deep learning approach, namely Residual and Spatial Learning based Channel Augmented Integrated CNN-Transformer architecture.<n>The proposed RS-CA-HSICT framework is composed of an HSICT block, a residual CNN module, a spatial CNN block, and a CA.<n> Experimental results on both the Kaggle benchmark and a diverse MPox dataset reported classification accuracy as high as 98.30% and an F1-score of 98.13%, which outperforms the existing CNNs and ViTs.
  arXiv  Detail & Related papers  (2025-11-19T14:32:34Z)
• Graph-based Multi-Modal Interaction Lightweight Network for Brain Tumor Segmentation (GMLN-BTS) in Edge Iterative MRI Lesion Localization System (EdgeIMLocSys) [6.451534509235736]
  We propose the Edge Iterative MRI Lesion Localization System (EdgeIMLocSys), which integrates Continuous Learning from Human Feedback.<n>Central to this system is the Graph-based Multi-Modal Interaction Lightweight Network for Brain Tumor (GMLN-BTS)<n>Our proposed GMLN-BTS model achieves a Dice score of 85.1% on the BraTS 2017 dataset with only 4.58 million parameters, representing a 98% reduction compared to mainstream 3D Transformer models.
  arXiv  Detail & Related papers  (2025-07-14T07:29:49Z)
• A Novel Channel Boosted Residual CNN-Transformer with Regional-Boundary Learning for Breast Cancer Detection [6.6149904114661]
  This study introduces a novel hybrid framework, CB-Res-RBCMT, combining customized residual CNNs and new ViT components for detailed BUSI cancer analysis.<n>The proposed RBCMT uses stem convolution blocks with CNN Meet Transformer (CMT) blocks, followed by new Regional and boundary (RB) feature extraction operations.<n>The proposed CB-Res-RBCMT achieves an F1-score of 95.57%, accuracy of 95.63%, sensitivity of 96.42%, and precision of 94.79% on the standard harmonized stringent BUSI dataset.
  arXiv  Detail & Related papers  (2025-03-19T08:59:02Z)
• CNN-Transformer Rectified Collaborative Learning for Medical Image Segmentation [60.08541107831459]
  This paper proposes a CNN-Transformer rectified collaborative learning framework to learn stronger CNN-based and Transformer-based models for medical image segmentation. Specifically, we propose a rectified logit-wise collaborative learning (RLCL) strategy which introduces the ground truth to adaptively select and rectify the wrong regions in student soft labels. We also propose a class-aware feature-wise collaborative learning (CFCL) strategy to achieve effective knowledge transfer between CNN-based and Transformer-based models in the feature space.
  arXiv  Detail & Related papers  (2024-08-25T01:27:35Z)
• L-SFAN: Lightweight Spatially-focused Attention Network for Pain Behavior Detection [44.016805074560295]
  Chronic Low Back Pain (CLBP) afflicts millions globally, significantly impacting individuals' well-being and imposing economic burdens on healthcare systems. While artificial intelligence (AI) and deep learning offer promising avenues for analyzing pain-related behaviors to improve rehabilitation strategies, current models, including convolutional neural networks (CNNs), have limitations. We introduce hbox EmoL-SFAN, a lightweight CNN architecture incorporating 2D filters designed to capture the spatial-temporal interplay of data from motion capture and surface electromyography sensors.
  arXiv  Detail & Related papers  (2024-06-07T12:01:37Z)
• HAGAN: Hybrid Augmented Generative Adversarial Network for Medical Image Synthesis [39.3485363570488]
  We propose the Hybrid Augmented Generative Adrial Network (HAGAN) to maintain the authenticity of structural texture and tissue cells. HAGAN contains Attention Mixed (AttnMix) Generator, Hierarchical Discriminator and Reverse Skip Connection between Discriminator and Generator.
  arXiv  Detail & Related papers  (2024-05-08T09:13:42Z)
• A Novel Feature Map Enhancement Technique Integrating Residual CNN and Transformer for Alzheimer Diseases Diagnosis [1.2432046687586285]
  Alzheimer diseases (ADs) involves cognitive decline and abnormal brain protein accumulation, necessitating timely diagnosis for effective treatment. CAD systems leveraging deep learning advancements have demonstrated success in AD detection but pose computational intricacies and the dataset minor contrast, structural, and texture variations. This approach integrates three distinct elements: a novel CNN Meet Transformer (HSCMT), customized residual learning CNN, and a new Feature Map Enhancement (FME) strategy to learn diverse morphological, contrast, and texture variations of ADs.
  arXiv  Detail & Related papers  (2024-03-30T10:17:13Z)
• GS-EMA: Integrating Gradient Surgery Exponential Moving Average with Boundary-Aware Contrastive Learning for Enhanced Domain Generalization in Aneurysm Segmentation [41.97669338211682]
  We propose a novel domain generalization strategy that employs gradient surgery exponential moving average (GS-EMA) optimization technique and boundary-aware contrastive learning (BACL) Our approach is distinct in its ability to adapt to new, unseen domains by learning domain-invariant features, thereby improving the robustness and accuracy of aneurysm segmentation across diverse clinical datasets.
  arXiv  Detail & Related papers  (2024-02-23T10:02:15Z)
• Spatial-Temporal DAG Convolutional Networks for End-to-End Joint Effective Connectivity Learning and Resting-State fMRI Classification [42.82118108887965]
  Building comprehensive brain connectomes has proved to be fundamental importance in resting-state fMRI (rs-fMRI) analysis. We model the brain network as a directed acyclic graph (DAG) to discover direct causal connections between brain regions. We propose Spatial-Temporal DAG Convolutional Network (ST-DAGCN) to jointly infer effective connectivity and classify rs-fMRI time series.
  arXiv  Detail & Related papers  (2023-12-16T04:31:51Z)
• Breast Ultrasound Tumor Classification Using a Hybrid Multitask CNN-Transformer Network [63.845552349914186]
  Capturing global contextual information plays a critical role in breast ultrasound (BUS) image classification. Vision Transformers have an improved capability of capturing global contextual information but may distort the local image patterns due to the tokenization operations. In this study, we proposed a hybrid multitask deep neural network called Hybrid-MT-ESTAN, designed to perform BUS tumor classification and segmentation.
  arXiv  Detail & Related papers  (2023-08-04T01:19:32Z)
• Brain Imaging-to-Graph Generation using Adversarial Hierarchical Diffusion Models for MCI Causality Analysis [44.45598796591008]
  Brain imaging-to-graph generation (BIGG) framework is proposed to map functional magnetic resonance imaging (fMRI) into effective connectivity for mild cognitive impairment analysis. The hierarchical transformers in the generator are designed to estimate the noise at multiple scales. Evaluations of the ADNI dataset demonstrate the feasibility and efficacy of the proposed model.
  arXiv  Detail & Related papers  (2023-05-18T06:54:56Z)
• Brain Tumor MRI Classification using a Novel Deep Residual and Regional CNN [0.0]
  A novel deep residual and regional-based Res-BRNet Convolutional Neural Network (CNN) is developed for effective brain tumor (Magnetic Resonance Imaging) MRI classification. The efficiency of the developed Res-BRNet is evaluated on a standard dataset; collected from Kaggle and Figshare containing various tumor categories. Experiments prove that the developed Res-BRNet outperforms the standard CNN models and attained excellent performances.
  arXiv  Detail & Related papers  (2022-11-29T20:14:13Z)
• Cross-Modality Deep Feature Learning for Brain Tumor Segmentation [158.8192041981564]
  This paper proposes a novel cross-modality deep feature learning framework to segment brain tumors from the multi-modality MRI data. The core idea is to mine rich patterns across the multi-modality data to make up for the insufficient data scale. Comprehensive experiments are conducted on the BraTS benchmarks, which show that the proposed cross-modality deep feature learning framework can effectively improve the brain tumor segmentation performance.
  arXiv  Detail & Related papers  (2022-01-07T07:46:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.