Hybrid CNN-Transformer Framework for Multi-Disease Detection from Medical Imaging Data
TL;DR
The proposed framework is intended to support clinical image assessment and prioritization rather than replace expert diagnosis, and demonstrates the potential of hybrid CNN-Transformer architectures for robust and scalable computer-assisted multi-disease screening from medical imaging data.
Abstract
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.