TransCat: a hybrid CNN-transformer network with KAN for medical image segmentation

Hybrid CNN-Transformer Framework for Multi-Disease Detection from Medical Imaging Data

Medical imaging plays an essential role in the early detection and clinical assessment of multiple diseases; however, manual image interpretation is time-consuming and can be affected by inter-observer variability, particularly when subtle pathological patterns are present. Conventional convolutional neural networks (CNNs) provide strong local feature extraction but may inadequately capture long-range spatial dependencies, whereas Vision Transformer-based architectures effectively model global contextual relationships but can require substantial training data. To exploit their complementary capabilities, this study proposes a Hybrid CNN-Transformer Framework for Multi-Disease Detection from Medical Imaging Data. The proposed architecture employs a multi-scale CNN backbone to extract local texture, boundary, morphological, and lesion-level characteristics, followed by Transformer-based self-attention to capture long-range dependencies among spatial feature representations. An attention-guided feature-fusion module integrates local CNN features with global Transformer representations, and the resulting discriminative embedding is processed by a multi-class classification layer for disease prediction. Data augmentation, class-aware training, and regularization are incorporated to improve robustness under heterogeneous medical-image distributions. Under the proposed experimental configuration, the hybrid framework achieves an overall accuracy of 96.74%, sensitivity of 95.92%, specificity of 97.18%, precision of 96.31%, F1-score of 96.11%, and area under the receiver operating characteristic curve (AUC) of 0.986. Compared with the selected standalone CNN baseline, the proposed approach provides approximately 5.2% relative improvement in accuracy and 5.8% improvement in F1-score. The combined local-global representation also improves discrimination of visually similar disease categories compared with individual CNN and Transformer models. These findings demonstrate the potential of hybrid CNN-Transformer architectures for robust and scalable computer-assisted multi-disease screening from medical imaging data. The proposed framework is intended to support clinical image assessment and prioritization rather than replace expert diagnosis.

CACFormer: A Hybrid CNN-Transformer Architecture Guided by Channel Attention for 3D Medical Image Segmentation.

Vision Transformer has achieved significant performance improvements in natural image segmentation tasks owing to its superior global modeling capabilities. However, applying vision Transformers to 3D medical image segmentation is challenging because of the quadratic computational complexity of the self-attention mechanism and their limited generalization on small-scale datasets. To address these limitations, we propose a hybrid CNN-Transformer architecture guided by channel attention, referred to as CACFormer, for 3D medical image segmentation. Specifically, we design a simple and effective channel attention module to guide the fusion of local and global features in each channel. This module adaptively assigns weights to each channel based on its semantic contribution to accurate segmentation. Meanwhile, we introduce a novel linear Transformer variant that integrates a linear attention mechanism with tanh activation. This design encourages the model to focus on the target regions and produce robust segmentation outcomes. The effectiveness and competitive generalization of the proposed framework are validated across five benchmark datasets. On AMOS2022, CACFormer achieves an average Dice score of 89.71%, outperforming 3D UX-Net (89.30%) while reducing inference time from 3.77 s to 2.49 s (a 33.95% reduction). On BraTS2021, CACFormer attains an average Dice score of 90.20%, comparable to TransBTS (90.33%), with 28.54% fewer parameters and 15.22% faster inference time (from 0.46 s to 0.39 s), demonstrating a favorable trade-off between performance and efficiency. Moreover, CACFormer demonstrates competitive cross-dataset generalization, achieving an average Dice score of 86.50% on BraTS2021 when trained on BraTS2019, significantly out-performing TransBTS (47.90%). Index Terms-3D Medical Image.

GTUNet: a GNN and transformer enhanced U-Net for coronary artery segmentation

GTUNet is proposed, a novel hybrid framework that synergistically integrates Graph Attention Networks, Transformers, and UNet for robust 3D medical image segmentation and significantly outperforms existing methods in ASSD, highlighting its precision in delineating vascular boundaries.

GLNet: global-to-local aware hybrid framework for medical image segmentation

MSCT-Trans: A Multi-scale Convolutional Neural Network Token Transformer for Interpretable Ultrasound Image Classification.

Automated ultrasound image classification is increasingly important for clinical decision support in breast, thyroid and fetal screening. However, deploying deep learning models in such safety-critical settings demands not only high predictive accuracy but also transparency, interpretability and trustworthiness-properties that existing approaches address insufficiently. Convolutional neural networks (CNNs) capture local texture patterns but struggle with global contextual dependencies, while Transformer-based models offer long-range reasoning yet require large-scale training data and remain sensitive to ultrasound-specific noise, both limiting factors for clinical deployment. We propose Multi-Scale CNN Token Transformer (MSCT-Trans), a lightweight and interpretable hybrid architecture for general-purpose ultrasound image classification. MSCT-Trans extracts multi-scale feature maps from a pre-trained CNN backbone and converts them into a unified token sequence, enabling a Transformer encoder to model global dependencies and inter-scale interactions over semantically meaningful, noise-attenuated representations. To support clinical transparency, we conducted a two-part explainability analysis-Gradient-weighted Class Activation Mapping++ spatial localisation and softmax class probability breakdown-demonstrating that MSCT-Trans consistently attends to diagnostically relevant anatomical regions, produces well-calibrated confidence estimates and associates prediction errors with model uncertainty rather than over-confident mis-classification. Here we evaluated MSCT-Trans on three ultrasound benchmarks spanning breast (BUS-BRA + BUSI + UCLM), thyroid (TN5000) and fetal imaging. MSCT-Trans consistently outperformed CNN and Transformer baselines across accuracy, macro-F1 and area under the receiver operating characteristic curve, particularly under class imbalance and limited data regimens. The combination of strong predictive performance, spatially grounded interpretability and calibrated uncertainty estimation positions MSCT-Trans as a transparent and trustworthy foundation for ultrasound-based clinical decision support. Code: https://github.com/MohsinFurkh/MSCT-Trans.

Comparative Study of CNN, Hybrid, and Transformer Architectures in Medical Image Classification.

The results show that larger models and larger pretraining datasets do not automatically lead to better downstream performance, and transfer effectiveness in medical imaging is driven primarily by architectural inductive biases, pretraining strategy, and domain relevance.