International Journal of Imaging Systems and Technology最新文献

{"title":"Federated Transfer Learning for Lung Disease Detection","authors":"Shrey Sumariya,&nbsp;Shreyas Rami,&nbsp;Shubham Revadekar,&nbsp;Chetashri Bhadane","doi":"10.1002/ima.70080","DOIUrl":"https://doi.org/10.1002/ima.70080","url":null,"abstract":"<div>\u0000 \u0000 <p>Detecting lung disease traditionally relied on the expertise of doctors and medical practitioners. However, advancements in Artificial Intelligence have revolutionized this process by utilizing machine learning and deep learning algorithms to analyze X-ray and CT scan data. Despite the potential of these technologies, the use of private patient data for training models poses significant privacy concerns, as hospitals are reluctant to share such sensitive information. To address this issue, this paper presents a decentralized approach using Federated Learning, which secures patient data while overcoming the limitations of centralized data collection and storage. We propose a Federated Transfer Learning system that allows for effective model training without centralizing sensitive data. This approach leverages the decentralized nature of federated learning and the efficiency of transfer learning, enabling us to train models with limited data from each hospital while minimizing computing costs. We evaluated four methodologies—centralized, federated, transfer learning, and federated transfer learning—to determine their effectiveness in classifying lung diseases. Our findings demonstrate that Federated Transfer Learning is the most effective method, as it preserves user privacy by training models directly on client devices and achieves high accuracy. Specifically, the ResNet-50 model yielded the highest performance, with accuracies of 87.95%, 88.04%, 87.55%, and 89.96% for the centralized, transfer, federated, and federated transfer learning approaches, respectively. This study underscores the potential of Federated Transfer Learning to enhance both the accuracy of disease classification and the protection of patient privacy in medical applications.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 3","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143840810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diff-CFFBNet: Diffusion-Embedded Cross-Layer Feature Fusion Bridge Network for Brain Tumor Segmentation","authors":"Xiaosheng Wu,&nbsp;Qingyi Hou,&nbsp;Chaosheng Tang,&nbsp;Shuihua Wang,&nbsp;Junding Sun,&nbsp;Yudong Zhang","doi":"10.1002/ima.70088","DOIUrl":"https://doi.org/10.1002/ima.70088","url":null,"abstract":"<div>\u0000 \u0000 <p>This study introduces the Diff-CFFBNet, a novel network for brain tumor segmentation designed to address the challenges of misdetection in broken tumor regions within MRI scans, which is crucial for early diagnosis, treatment planning, and disease monitoring. The proposed method incorporates a cross-layer feature fusion bridge (CFFB) to enhance feature interaction and a cross-layer feature fusion U-Net (CFFU-Net) to reduce the semantic gap in diffusion models. Additionally, a sampling-quantity-based fusion (SQ-Fusion) is utilized to leverage the uncertainty of diffusion models for improved segmentation outcomes. Experimental validation on BraTS 2019, BraTS 2020, TCGA-GBM, TCGA-LGG, and MSD datasets demonstrates that Diff-CFFBNet outperforms existing methods, achieving superior performance in terms of Dice score, HD95, and mIoU metrics. These results indicate the model's robustness and precision, even under challenging conditions with complex tumor structures. Diff-CFFBNet provides a reliable solution for accurate and efficient brain tumor segmentation in medical imaging, with the potential for clinical application in treatment planning and disease monitoring. Future work aims to extend this approach to multiple tumor types and refine diffusion model applications in medical image segmentation.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 3","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feature Subspace Projection Knowledge Distillation for Medical Image Segmentation","authors":"Xiangchun Yu,&nbsp;Qiaoyi Chen,&nbsp;Miaomiao Liang,&nbsp;Lingjuan Yu,&nbsp;Jian Zheng","doi":"10.1002/ima.70085","DOIUrl":"https://doi.org/10.1002/ima.70085","url":null,"abstract":"<div>\u0000 \u0000 <p>Feature-based knowledge distillation facilitates feature knowledge transfer by aligning intermediate features of students and high-performance teachers such as TranUnet and MISSFormer in medical image segmentation. However, the bias-variance coupling resulting from redundancy or noise within high-dimensional features presents a significant challenge for effective knowledge transfer. To tackle this issue, we propose a feature subspace projection knowledge distillation (FSP-KD) method to decouple bias and variance in the high-dimensional feature space. This method decomposes the feature space into two components: the variance-dependent distribution and the bias-dependent distribution. The bias-dependent distribution is modeled as a weighted post-projection feature distribution using the feature subspace projection (FSP) module. Likewise, the variance-dependent distribution is represented by a weighted pre-projection feature distribution. Additionally, a conditional adversarial mechanism (CADV) module is integrated at the logits layer to prompt the student to identify higher-order discrepancies from the teacher. This approach leverages conditional generative adversarial learning to improve the holistic alignment between student and teacher distributions. Extensive experiments are carried out on three benchmark datasets for medical image segmentation: Synapse, Flare2022, and m2caiSeg. The experimental results show that our proposed FSP-KD method has achieved state-of-the-art performance. Notably, FSP-KD has outperformed the teacher MISSFormer when used in a teacher-student setup with ResNet18. Ablation experiments and visualization results provide additional confirmation of the effectiveness of each module.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 3","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Explainable AI for Blood Image Classification With Dynamic CNN Model Selection Framework","authors":"Datenji Sherpa,&nbsp;Dibakar Raj Pant","doi":"10.1002/ima.70084","DOIUrl":"https://doi.org/10.1002/ima.70084","url":null,"abstract":"<div>\u0000 \u0000 <p>Explainable AI (XAI) frameworks are becoming essential in many areas, including the medical field, as they help us to understand AI decisions, increasing clinical trust and improving patient care. This research presents a robust and comprehensive Explainable AI framework. To classify images from the BloodMNIST and Raabin-WBC datasets, various pre-trained convolutional neural network (CNN) architectures: the VGG, the ResNet, the DenseNet, the EfficientNet, the MobileNet variants, the SqueezeNet, and the Xception are implemented both individually and in combination with SpinalNet. For parameter analysis, four models, VGG16, VGG19, ResNet50, and ResNet101, were combined with SpinalNet. Notably, these SpinalNet hybrid models significantly reduced the model parameters while maintaining or even improving the model accuracy. For example, the VGG 16 + SpinalNet shows a 40.74% parameter reduction and accuracy of 98.92% (BloodMnist) and 98.32% (Raabin-WBC). Similarly, the combinations of VGG19, ResNet50, and ResNet101 with SpinalNet resulted in weight parameter reductions by 36.36%, 65.33%, and 52.13%, respectively, with improved accuracy for both datasets. These hybrid SpinalNet models are highly efficient and well-suited for resource-limited environments. The authors have developed a dynamic model selection framework. This framework optimally selects the best models based on prediction scores, prioritizing lightweight models in cases of ties. This method guarantees that for every input, the most effective model is used, which results in higher accuracy as well as better outcomes. Explainable AI (XAI) techniques: Local Interpretable Model-agnostic Explanations (LIME), SHapley Additive ExPlanations (SHAP), and Gradient-weighted Class Activation Mapping (Grad-CAM) are implemented. These help us to understand the key features that influence the model predictions. By combining these XAI methods with dynamic model selection, this research not only achieves excellent accuracy but also provides useful insights into the elements that influence model predictions.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 3","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143818781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EDenseNetViT: Leveraging Ensemble Vision Transform Integrated Transfer Learning for Advanced Differentiation and Severity Scoring of Tuberculosis","authors":"Mamta Patankar,&nbsp;Vijayshri Chaurasia,&nbsp;Madhu Shandilya","doi":"10.1002/ima.70082","DOIUrl":"https://doi.org/10.1002/ima.70082","url":null,"abstract":"<div>\u0000 \u0000 <p>Lung infections such as tuberculosis (TB), COVID-19, and pneumonia share similar symptoms, making early differentiation challenging with x-ray imaging. This can delay correct treatment and increase disease transmission. The study focuses on extracting hybrid features using multiple techniques to effectively distinguish between TB and other lung infections, proposing several methods for early detection and differentiation. To better diagnose TB, the paper presented an ensemble DenseNet with a Vision Transformer (ViT) network (EDenseNetViT). The proposed EDenseNetViT is an ensemble model of Densenet201 and a ViT network that will enhance the detection performance of TB with other lung infections such as pneumonia and COVID-19. Additionally, the EDenseNetViT is extended to predict the severity level of TB. This severity score approach is based on combined weighted low-level features and high-level features to show the severity level of TB as mild, moderate, severe, and fatal. The result evaluation was conducted using chest image datasets, that is Montgomery Dataset, Shenzhen Dataset, Chest x-ray Dataset, and COVID-19 Radiography Database. All data are merged and approx. Seven thousand images were selected for experimental design. The study tested seven baseline models for lung infection differentiation. Initially, DenseNet transfer learning models, including DenseNet121, DenseNet169, and DenseNet201, were assessed, with DenseNet201 performing the best. Subsequently, DenseNet201 was combined with Principal component analysis (PCA) and various classifiers, with the combination of PCA and random forest classifier proving the most effective. However, the EDenseNetViT model surpassed all and achieved approximately 99% accuracy in detecting TB and distinguishing it from other lung infections like pneumonia and COVID-19. The proposed EdenseNetViT model was used for classifying TB, Pneumonia, and COVID-19 and achieved an average accuracy of 99%, 98%, and 96% respectively. Compared to other existing models, EDenseNetViT outperformed the best.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 3","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143818780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing 3D Global and Local Feature Extraction for Pneumonia Multilesion Segmentation","authors":"Huiyao He,&nbsp;Yinwei Zhan,&nbsp;Yulan Yan,&nbsp;Yuefu Zhan","doi":"10.1002/ima.70083","DOIUrl":"https://doi.org/10.1002/ima.70083","url":null,"abstract":"<div>\u0000 \u0000 <p>Precise segmentation of pneumonia lesions using deep learning has been a research focus in medical image segmentation, in which convolutional neural networks (CNNs) excel at capturing local features through convolutional layers but struggle with global information, while Transformers handle global features and long-range dependencies well but require substantial computational resources and data. Motivated by the recently introduced Mamba that effectively models long-range dependencies with less complexity, we develop a novel network architecture in order to simultaneously enhance the handling of both global and local features. It integrates an enhanced Mamba module SE3DMamba to improve the extraction of three-dimensional global features and a medical version of deep residual convolution MDRConv to enhance the extraction of local features with a self-configuring mechanism. Experiments conducted on two pneumonia CT datasets, including the pneumonia multilesion segmentation dataset (PMLSegData) with three lesion types—consolidations, nodules, and cavities—and MosMedData of ground-glass opacifications demonstrate that our network surpasses state-of-the-art CNN and Transformer-based segmentation models across all tasks, advancing the clinical feasibility of deep learning for pneumonia multilesion segmentation.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 3","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143809784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual-Resonant RF Coil for Proton and Phosphorus Imaging at 7 Tesla MRI","authors":"Ashraf Abuelhaija,&nbsp;Gameel Saleh,&nbsp;Emad Awada,&nbsp;Sanaa Salama,&nbsp;Samer Issa,&nbsp;Osama Nashwan","doi":"10.1002/ima.70081","DOIUrl":"https://doi.org/10.1002/ima.70081","url":null,"abstract":"<div>\u0000 \u0000 <p>Magnetic resonance spectroscopy (MRS) provides a non-invasive method for examining metabolic alterations associated with diseases. While ¹H-based MRS is commonly employed, its effectiveness is often limited by signal interference from water, reducing the accuracy of metabolite differentiation. In contrast, X-nuclei MRS leverages the broader chemical shift dispersion of non-hydrogen nuclei to enhance the ability to distinguish between metabolites. This article presents the design and analysis of a dual-resonant meandered coil for 7 Tesla magnetic resonance imaging (MRI), to simultaneously help in image hydrogen protons (¹H) and detect Phosphorus (³¹P) atomic nuclei at 298 MHz and 120.6 MHz, respectively. Both single-channel and four-channel configurations were designed and analyzed. The single-channel coil integrates an LC network for dual resonance, achieving excellent impedance matching (S₁₁ &lt; −10 dB) and a homogeneous magnetic field distribution within the region of interest. A transmission-line-based matching network was implemented to optimize performance at both frequencies. The four-channel coil was simulated using CST Microwave Studio and experimentally validated. Simulations demonstrated impedance matching and minimal mutual coupling of −38 dB at 298 MHz and −24 dB at 120.6 MHz. The measured S-parameters confirmed these results, showing high decoupling and robust performance across all channels. The prototype featured integrated LC networks and optimized meander structures, ensuring efficient power transmission and uniform field distribution. This work highlights the effectiveness of the proposed dual-resonant coil designs for MRS applications, offering promising potential for advanced clinical diagnostics.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 3","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143809754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rectal Cancer Segmentation: A Methodical Approach for Generalizable Deep Learning in a Multi-Center Setting","authors":"Jovana Panic,&nbsp;Arianna Defeudis,&nbsp;Lorenzo Vassallo,&nbsp;Stefano Cirillo,&nbsp;Marco Gatti,&nbsp;Roberto Sghedoni,&nbsp;Michele Avanzo,&nbsp;Angelo Vanzulli,&nbsp;Luca Sorrentino,&nbsp;Luca Boldrini,&nbsp;Huong Elena Tran,&nbsp;Giuditta Chiloiro,&nbsp;Giuseppe Roberto D'Agostino,&nbsp;Enrico Menghi,&nbsp;Roberta Fusco,&nbsp;Antonella Petrillo,&nbsp;Vincenza Granata,&nbsp;Martina Mori,&nbsp;Claudio Fiorino,&nbsp;Barbara Alicja Jereczek-Fossa,&nbsp;Marianna Alessandra Gerardi,&nbsp;Serena Dell'Aversana,&nbsp;Antonio Esposito,&nbsp;Daniele Regge,&nbsp;Samanta Rosati,&nbsp;Gabriella Balestra,&nbsp;Valentina Giannini","doi":"10.1002/ima.70076","DOIUrl":"https://doi.org/10.1002/ima.70076","url":null,"abstract":"<p>Noninvasive Artificial Intelligence (AI) techniques have shown great potential in assisting clinicians through the analysis of medical images. However, significant challenges remain in integrating these systems into clinical practice due to the variability of medical data across multi-center databases and the lack of clear implementation guidelines. These issues hinder the ability to achieve robust, reproducible, and statistically significant results. This study thoroughly analyzes several decision-making steps involved in managing a multi-center database and developing AI-based segmentation models, using rectal cancer as a case study. A dataset of 1212 Magnetic Resonance Images (MRIs) from 14 centers was used. The study examined the impact of different image normalization techniques, network hyperparameters, and training set compositions (in terms of size and construction strategies). The findings emphasize the critical role of image normalization in reducing variability and improving performance. Additionally, the study underscores the importance of carefully selecting network structures and loss functions based on the desired outcomes. The potential of clustering approaches to identify representative training subsets, even with limited data sizes, was also evaluated. While no definitive preprocessing pipeline was identified, several networks developed during the study produced promising results on the external validation set. The insights and methodologies presented may help raise awareness and promote more informed decisions when implementing AI systems in medical imaging.</p>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 3","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ima.70076","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143762100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modal Feature Supplementation Enhances Brain Tumor Segmentation","authors":"Kaiyan Zhu,&nbsp;Weiye Cao,&nbsp;Jianhao Xu,&nbsp;Tong Liu,&nbsp;Yue Liu,&nbsp;Weibo Song","doi":"10.1002/ima.70079","DOIUrl":"https://doi.org/10.1002/ima.70079","url":null,"abstract":"<div>\u0000 \u0000 <p>For patients with brain tumors, effectively utilizing the complementary information between multimodal medical images is crucial for accurate lesion segmentation. However, effectively utilizing the complementary features across different modalities remains a challenging task. To address these challenges, we propose a modal feature supplement network (MFSNet), which extracts modality features simultaneously using both a main and an auxiliary network. During this process, the auxiliary network supplements the modality features of the main network, enabling accurate brain tumor segmentation. We also design a modal feature enhancement module (MFEM), a cross-layer feature fusion module (CFFM), and an edge feature supplement module (EFSM). MFEM enhances the network performance by fusing the modality features from the main and auxiliary networks. CFFM supplements additional contextual information by fusing features from adjacent encoding layers at different scales, which are then passed into the corresponding decoding layers. This aids the network in preserving more details during upsampling. EFSM improves network performance by using deformable convolution to extract challenging boundary lesion features, which are then used to supplement the final output of the decoding layer. We evaluated MFSNet on the BraTS2018 and BraTS2021 datasets. The Dice scores for the whole tumor, tumor core, and enhancing tumor regions were 90.86%, 90.59%, 84.72%, and 92.28%, 92.47%, 86.07%, respectively. This validates the accuracy of MFSNet in brain tumor segmentation, demonstrating its superiority over other networks of similar type.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 3","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143770075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interactive CNN and Transformer-Based Cross-Attention Fusion Network for Medical Image Classification","authors":"Shu Cai,&nbsp;Qiude Zhang,&nbsp;Shanshan Wang,&nbsp;Junjie Hu,&nbsp;Liang Zeng,&nbsp;Kaiyan Li","doi":"10.1002/ima.70077","DOIUrl":"https://doi.org/10.1002/ima.70077","url":null,"abstract":"<div>\u0000 \u0000 <p>Medical images typically contain complex structures and abundant detail, exhibiting variations in texture, contrast, and noise across different imaging modalities. Different types of images contain both local and global features with varying expressions and importance, making accurate classification highly challenging. Convolutional neural network (CNN)-based approaches are limited by the size of the convolutional kernel, which restricts their ability to capture global contextual information effectively. In addition, while transformer-based models can compensate for the limitations of convolutional neural networks by modeling long-range dependencies, they are difficult to extract fine-grained local features from images. To address these issues, we propose a novel architecture, the Interactive CNN and Transformer for Cross Attention Fusion Network (IFC-Net). This model leverages the strengths of CNNs for efficient local feature extraction and transformers for capturing global dependencies, enabling it to preserve local features and global contextual relationships. Additionally, we introduce a cross-attention fusion module that adaptively adjusts the feature fusion strategy, facilitating efficient integration of local and global features and enabling dynamic information exchange between the CNN and transformer components. Experimental results on four benchmark datasets, ISIC2018, COVID-19, and liver cirrhosis (line array, convex array), demonstrate that the proposed model achieves superior classification performance, outperforming both CNN and transformer-only architectures.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 3","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143749366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}