Attention-guided hybrid CNN–transformer framework for cross-modality COVID-19 detection and lesion localization in CT and chest X-ray images

Accurate and interpretable analysis of thoracic medical images is essential for reliable COVID-19 diagnosis; however, existing approaches face significant challenges in jointly performing classification and lesion localization across multiple imaging modalities. Suboptimal diagnostic knowledge and limited spatial awareness are the results of conventional deep learning approaches, especially models based on convolutional neural networks (CNNs). These methods mainly concentrate on local feature extraction and often handle classification and segmentation as independent tasks. Although transformer-based models enhance global contextual learning, their practical use in real-world clinical settings is limited due to their high computing resource requirements and large-scale dataset requirements. On top of that, there are currently no explainability procedures built into hybrid CNN-Transformer methods, and most of them use shallow or one-directional fusion algorithms. In order to overcome these shortcomings, this research suggests a CNN-Transformer hybrid attention-guided paradigm for COVID-19 identification and lesion localization across modalities utilizing separate CT and CXR datasets. The suggested design incorporates a ResNet-18 backbone for local feature extraction, a light Vision Transformer for global contextual modelling, a bidirectional cross attention fusion module for effective interaction between CNN and transformer representations, a feature refinement mechanism based on convolutional block attention module (CBAM), and a unified multi-task learning framework. In the CT data set, the experimental results prove that the proposed model is superior to the other models, having an accuracy of 98.29%, a precision of 98.14%, a recall of 98.13%, and an F1-Score of 98.11%. The model also has a Dice score of 0.847 and an Intersection-over-Union score of 0.848, respectively. The proposed model gets an accuracy of 97.74%, a precision of 97.93%, a recall of 97.52%, and an F1-Score. By enhancing generalizability and clinical reliability across imaging modalities, our findings demonstrate that the suggested paradigm delivers accurate, robust, and interpretable COVID-19 diagnosis.