International Journal of Imaging Systems and Technology最新文献

{"title":"Advancing Cervical Cancer Classification Through a Fusion of CNN and Vision Transformer Models","authors":"Fida Hussain Dahri,&nbsp;Ghulam Mustafa,&nbsp;Ashfaque Khowaja,&nbsp;Awais Khan Jumani,&nbsp;Vania V. Estrela,&nbsp;Asif Ali Laghari","doi":"10.1002/ima.70352","DOIUrl":"10.1002/ima.70352","url":null,"abstract":"<div>\u0000 \u0000 <p>Cervical cancer remains a major contributor to cancer-related mortality among women worldwide, with a disproportionately high burden in low- and middle-income countries. Pap smear imaging is a standard screening modality for detecting precancerous and malignant cervical abnormalities; however, manual interpretation is labor-intensive, subjective, and susceptible to interobserver variability. To mitigate these limitations, this study proposes a hybrid deep learning framework for automated cervical cell classification that integrates Vision Transformers (ViT) with Convolutional Neural Networks (CNN). The proposed framework incorporates a structured preprocessing pipeline, including image resampling and data augmentation strategies such as random horizontal flipping and controlled rotations, to enhance model generalization and mitigate overfitting. Input images are divided into fixed-size patches and processed through a ViT backbone to capture long-range contextual dependencies. Complementary CNN layers are employed to extract localized morphological features critical for cytological analysis. The extracted representations are combined through a feature fusion mechanism and passed to fully connected layers for classification. The ViT component is initialized with pretrained weights and subsequently fine-tuned on cervical cytology datasets. Experimental evaluation on the Herlev and SIPaKMeD datasets achieved classification accuracies of 97.31% and 96.62%, respectively. Ablation analysis showed that the CNN branch improves local morphological feature discrimination, while class-wise evaluation indicated stable performance across multiple cytological categories. These results support the effectiveness of the proposed CNN–ViT fusion framework for automated cervical cell classification and motivate further validation on larger and patient-indexed clinical datasets.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"36 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147668164","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":"Advanced Machine Learning and Deep Learning Approaches for Accurate Epileptic Seizure Detection Using EEG Signals","authors":"Malika Garg,&nbsp;Jasbir Kaur,&nbsp;Neelam Rup Prakash","doi":"10.1002/ima.70342","DOIUrl":"10.1002/ima.70342","url":null,"abstract":"<div>\u0000 \u0000 <p>Epilepsy, a neurological disorder that is caused by improper brain activity that results in seizures, is prevalent in millions of persons across the world and for which diagnosis is extremely crucial for treatment. Early detection of epileptic seizures is paramount as it enables timely intervention, improves quality of life, and prevents potential risks during seizures. Machine learning (ML) and deep learning (DL) algorithms have emerged as powerful tools in revolutionizing medical field, particularly in domains involving images, signals, and other types of visual representations. In our study, we have utilized the capability of these algorithms in exploring their impact on the epileptic seizure detection with the help of EEG Signals. Nine DL models, namely LSTM, GRU, Bi-LSTM, CNN, FCNN, Hybrid CNN-LSTM, EEGNet, Shallow ConvNet, and Hybrid CNN-GRU and seven machine learning models that is, Random Forest, XGBoost, KNN, Logistic Regression, Naïve Bayes, Decision Tree, and Stacking Ensemble are employed for classification of epileptic seizures on EEG signal dataset. All the models have been evaluated using the standard performance metrics for determining the effectiveness of these models on the epileptic seizure classification as epileptic and nonepileptic. Visual representations like accuracy-loss graphs and confusion matrix were also generated for better visual understanding of the performance of the model. Among all DL Models, CNN emerged as the best performing model with 85% accuracy, whereas, among ML models, XGBoost performs best with accuracy of 88%. The study underscores the potential and effectiveness of ML and DL models in detecting complex patterns and generating predictive insights for classification and detection of epileptic seizure from EEG signals.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"36 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147668040","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":"SPICE: Leveraging Soft Probabilistic Causal Intervention for Breast Ultrasound Tumor Segmentation","authors":"Haobo Chen,&nbsp;Changyan Wang,&nbsp;Qi Zhang","doi":"10.1002/ima.70350","DOIUrl":"10.1002/ima.70350","url":null,"abstract":"<div>\u0000 \u0000 <p>Accurate segmentation of breast tumors in ultrasound images is essential for clinical diagnosis and treatment planning. However, ultrasound imaging is inherently affected by speckle noise, low contrast, and scanner-specific artifacts, which can introduce spurious correlations and limit the reliability of conventional deep learning models. To address these challenges, we propose SPICE (Soft Probabilistic Intervention for Causal sEgmentation), a causal intervention framework that explicitly disentangles anatomy-consistent causal features from unstable imaging confounders in breast ultrasound images. SPICE adopts probabilistic causal-confounding disentanglement with dual-branch supervision to structurally separate lesion-related and acquisition-dependent representations. Additionally, causal-confounding contrastive learning and multiscale causal consistency regularization are incorporated to enhance feature discriminability and stability. Experiments on five public datasets demonstrate that SPICE achieves superior segmentation performance with the Dice similarity coefficients of 88.42% on the internal validation cohort and 89.49% and 84.14% on two external cohorts, outperforming other state-of-the-art methods. SPICE also provides explicit causal and confounding outputs, enabling interpretable and uncertainty-aware predictions. These results indicate that structured causal intervention enhances both segmentation accuracy and reliability in breast ultrasound imaging.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"36 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147668103","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":"Multi-Scale Lightweight 3D MRI Brain Tumor Segmentation Algorithm Based on Attention Mechanism","authors":"Tongyuan Huang,&nbsp;Huayu Chen,&nbsp;Chunyuan Liu,&nbsp;Yihan Yang,&nbsp;Zipeng Wu","doi":"10.1002/ima.70349","DOIUrl":"10.1002/ima.70349","url":null,"abstract":"<div>\u0000 \u0000 <p>This study proposes a lightweight multi-scale 3D MRI brain tumor segmentation algorithm based on an attention mechanism. The aim is to address two major challenges faced by current three-dimensional convolutional neural networks (3D CNNs) in practical applications: the limited ability to capture shallow features and the low deployment efficiency caused by excessive model parameters. Building upon the 3D U-Net architecture, this study introduces three innovative improvements: (1) In the encoder, conventional convolutions are replaced with a multi-scale feature fusion attention mechanism, enhancing the network's capability to capture shallow information. (2) In the decoder, a residual decoupled convolution is incorporated to reduce the number of parameters while preserving rich multi-scale feature representations, thus improving segmentation performance. (3) In the output layer, a hierarchical attention fusion module is proposed to integrate multi-channel information, which strengthens tumor boundaries and local details, further enhancing segmentation accuracy. The proposed model contains only 3.5 million parameters and requires 27.58 GFLOPs for computation. Extensive experiments conducted on the BraTS 2018 and BraTS 2020 datasets demonstrate significant performance improvements. In BraTS 2018, the DSC scores for the enhanced tumor, the whole tumor, and the tumor core reached 80.34%, 90.12%, and 84.77%, respectively; while in BraTS 2020, the corresponding scores were 79.14%, 88.22%, and 85.79%. These results indicate that the proposed model has strong potential for practical application and demonstrates excellent clinical feasibility as a lightweight solution for brain tumor segmentation.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"36 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147668098","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":"A Feature-Free Image-to-Patient Registration Method for Image-Guided Neurosurgery","authors":"Zhichao Li,&nbsp;Hao Ren,&nbsp;Wei Zhou,&nbsp;Xiaodong Ma,&nbsp;Dan Wu","doi":"10.1002/ima.70344","DOIUrl":"10.1002/ima.70344","url":null,"abstract":"<div>\u0000 \u0000 <p>Image-guided neurosurgery system (IGNS) has become an indispensable component of modern precise neurosurgery. Image-to-patient registration plays a key role in IGNS, as it directly impacts accuracy and security of the surgery. We present a novel surface-based image-to-patient registration method for IGNS, which is feature-free and performs direct registration. First, the bounding boxes of the surfaces are computed, and a convex plane-based approach is developed to obtain an initial posture close to the desired value. Then, a constraint-based dimensionality reduction strategy is proposed to identify the most optimal posture for coarse registration. Finally, the iterative closest point (ICP) algorithm is employed to refine and generate the final transformation. This registration method is evaluated using the phantom models. The results demonstrate that our method produces transformations comparable to the currently widely used method. The proposed feature-free method exhibits the potential to facilitate IGNS with sufficient precision.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"36 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147668845","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":"Integrating Fuzzy Scoring for Patching With Ensemble Learning to Refine Histopathological Subtyping of Lung Adenocarcinoma","authors":"Mohammad Mehdi Hosseini,&nbsp;Meghdad Sabouri Rad,&nbsp;Junze (Vincent) Huang,&nbsp;Rakesh Choudhary,&nbsp;Harmen Siezen,&nbsp;Saverio J. Carello,&nbsp;Ola El-Zammar,&nbsp;Michel R. Nasr,&nbsp;Bardia Rodd","doi":"10.1002/ima.70336","DOIUrl":"10.1002/ima.70336","url":null,"abstract":"<div>\u0000 \u0000 <p>Automated histopathological subtyping of lung cancer using stained whole-slide images (WSIs) remains a pivotal yet formidable challenge, owing to pronounced tumor heterogeneity, intricate cellular morphology, and severe class imbalance within available datasets. Deep learning architectures vary in their capacity to capture diverse pathomic features, and the diagnostic efficacy of such models is heavily influenced by the quality of the tissue patches extracted from WSIs. To address these limitations, we present a novel ensemble deep learning framework enhanced by a fuzzy-weighted patch quality assessment, which improves the selection and contribution of informative regions within WSIs. High-quality patches are identified using a fuzzy scoring method and processed through multiple pretrained CNN and transformer models to capture diverse feature representations. These features are integrated via latent embeddings, with fuzzy scores incorporated both as auxiliary inputs and as weights in the training loss function to emphasize clinically relevant tissue regions. Our approach achieved a 1.5% and 1.4% performance gain over current state-of-the-art methods, achieving 96.1% and 93.0% on the BMIRDS-LUAD and WSSS4LUAD datasets, respectively, demonstrating improved robustness in subtype classification and potential for integration into computational pathology workflows. By weighting patches according to histologic representativeness, the method aligns with pathologist workflow and is suitable for integration into in silico decision support for patient stratification.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"36 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147668727","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":"A Clinical Decision Support System Based on Machine Learning: Toward High Detection Performance for Ligament Injury","authors":"Cheikh Salmi,&nbsp;Nour El-Houda Senoussi,&nbsp;Akram Lebcir","doi":"10.1002/ima.70348","DOIUrl":"10.1002/ima.70348","url":null,"abstract":"<div>\u0000 \u0000 <p>Computer Vision (CV) is gaining traction in various fields of medicine, with one of its most significant applications being the development of medical diagnostic tools. In the domain of trauma and orthopedics, anterior cruciate ligament (ACL) tears, particularly among athletes such as footballers and skiers, are a common clinical problem. Most clinicians in this field have access to Magnetic Resonance Imaging (MRI), which is routinely performed by radiologists with specialized training in the interpretation of high-definition knee scans. However, the diagnostic process is influenced by many factors that can compromise the reliability of clinical assessment, including cognitive fatigue, excessive time, and poor imaging modalities that reduce the reliability of the obtained images. This work proposes a diagnostic system to automate the analysis of MR image scans of the knee joints using Convolutional Neural Networks (CNNs). The system is designed to help practitioners automatically locate and analyze the ACL region and provide an assessment of pathological changes in the ligament. The solution is implemented with two connected CNNs, one dedicated to localizing the ACL within volumetric MR image data, and the other responsible for performing a classification that separates intact ligaments from those that are ruptured. The proposed methodology is validated on a curated dataset of 917 knee MR examinations obtained from the Rijeka Clinical Hospital Centre in Croatia. Each examination contains 32 sagittal slices with a spatial resolution of 320 by 320 pixels. A standard sequence of preprocessing steps was carried out to normalize the data prior to training in order to organize the input data. The entire classification pipeline is integrated into a diagnostic platform accessible via the web and Android, which aids clinical users in assessing the integrity of ACL injury in real time. Regarding the experimental results, the proposed system was able to achieve a classification accuracy of roughly 97.80%, well above the results achieved by traditional machine learning classifiers such as Support Vector Machines (SVM) and Random Forests (RF). The demonstrated efficacy of the system significantly strengthens its potential as a comprehensive tool to assist clinicians in evaluating ACL injuries and radiological evaluations of ACL injuries.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"36 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147668726","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":"A Dual-Branch Self-Enhancing Network for Lumbar Disc Herniation Lesion Segmentation and Classification","authors":"Jianpeng Chen,&nbsp;Yida Wang,&nbsp;Xuning Zhang,&nbsp;Changlin Lv,&nbsp;Yongming Xi,&nbsp;Huan Yang","doi":"10.1002/ima.70347","DOIUrl":"10.1002/ima.70347","url":null,"abstract":"<div>\u0000 \u0000 <p>Lumbar Disc Herniation (LDH) is characterized by a complex anatomical structure and significant morphological variability on magnetic resonance imaging (MRI). These characteristics, in turn, present significant challenges for automated diagnosis. To address this, this paper proposes a novel multi-task deep learning architecture, the Dual-Branch Self-Enhancing Network (DBSE-Net), designed to simultaneously perform key structure segmentation and disc classification on LDH MRI, thus improving the efficiency of computer-aided diagnosis. DBSE-Net first constructs a Frequency Self-Enhancing Encoder (FSE Encoder), composed of stacked Frequency Self-Enhancing Block (FSE Block). Each block uses Frequency-Enhanced Convolution (FE-Conv) to extract multi-scale frequency domain features and focuses on diagnostically critical areas through an attention mechanism. Additionally, to handle anatomical variations and structural changes, the model incorporates Deformable Convolution to enhance the representation of key features. DBSE-Net uses a dual-branch decoder to effectively decouple the segmentation and classification tasks. The Spatial decoder accurately segments structures such as the intervertebral disc, spinal canal, and spinous process, while the Semantic decoder refines the morphological features of the intervertebral disc for herniated and non-herniated disc classification. On the LA-MRI dataset, DBSE-Net achieves 91.95% mean Dice Similarity Coefficient (mDSC) and 85.47% mean Intersection-over-Union (mIoU) for segmentation, and 97.67% accuracy (ACC) and 98.67% area under the curve (AUC) for classification, outperforming state-of-the-art methods. These results demonstrate that DBSE-Net holds strong potential for intelligent diagnosis in LDH MRI and can be extended to 3D medical image analysis tasks.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"36 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147668728","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":"A Multiscale Intracerebral Hemorrhage Segmentation Framework Based on Wavelet Convolution and Feature Compensation Mechanism","authors":"Wenyu Wang,&nbsp;Huiyun Long,&nbsp;Fangfang Gou,&nbsp;Guangqian Kong,&nbsp;Xun Duan","doi":"10.1002/ima.70345","DOIUrl":"https://doi.org/10.1002/ima.70345","url":null,"abstract":"<div>\u0000 \u0000 <p>Artificial intelligence–assisted diagnostic technologies are increasingly applied in the medical field, particularly for the diagnosis of intracerebral hemorrhage (ICH), a cerebrovascular disease with high mortality and disability rates. Although CT imaging is the primary modality for ICH diagnosis, accurate and rapid lesion segmentation remains challenging because hemorrhagic lesions are small, spatially variable, and heterogeneous in appearance, and conventional models are limited in detecting small lesions due to their small receptive fields and feature loss. To address these challenges, we propose WRes-UNet (Wavelet Convolution Residual-UNet), an enhanced U-Net–based segmentation framework that integrates Wavelet Transform Convolution (WTConv) and an Adaptive Feature Shortcut (AFS) mechanism. WTConv decomposes feature maps into multiple frequency subbands, enabling multiscale contextual representation while preserving critical low-frequency information. The AFS module adaptively enhances discriminative channel features, effectively compensating for feature loss and improving the localization of small hemorrhagic regions. Extensive experiments on an ICH CT dataset show that WRes-UNet consistently outperforms state-of-the-art segmentation models in Dice, IoU, and F1 scores, while achieving lower model complexity. In particular, the proposed framework demonstrates clear advantages in segmenting small and irregular hemorrhagic lesions. These resultsindicate that WRes-UNet provides an effective and robust solution for precise ICH segmentation, showing promising clinical value for early diagnosis. The code for WRes-UNet is available at https://github.com/GZWANGWENYU/WRes-UNet.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"36 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147615230","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-Branch Multimodal Attention Fusion Networks for Electrical Impedance and Microwave Dual-Mode Tomography of Stroke","authors":"Jinzhen Liu,&nbsp;Xiangqian Meng,&nbsp;Hui Xiong","doi":"10.1002/ima.70337","DOIUrl":"https://doi.org/10.1002/ima.70337","url":null,"abstract":"<div>\u0000 \u0000 <p>Electrical impedance tomography (EIT) and microwave tomography (MWT), as two emerging noninvasive imaging techniques, have been widely used in stroke diagnosis. However, single-modality imaging exhibits inherent limitations such as insufficient information and low resolution, which pose challenges in meeting the requirements of clinical diagnosis. To enhance the resolution of stroke imaging, a dual-branch multimodal attention fusion network (DMAFusion) for electrical impedance and microwave dual-mode tomography (EI/MDT) is proposed. The network employs a dual-branch interactive encoder module to train the encoders separately for different modalities while enabling cross-modal information fusion. With an improved efficient multiscale attention module, the module enhances feature extraction capabilities. An attentional feature fusion strategy is applied to deeply fuse features from different modalities, obtaining more comprehensive and accurate information. Through comparative and robustness experiments, the results demonstrate that DMAFusion outperforms single-modality imaging methods, achieving higher-resolution reconstructed images and improved robustness. Additionally, compared to other multimodal imaging networks, the proposed method significantly enhances image quality and reconstruction accuracy, further validating the effectiveness of EI/MDT and the superiority of DMAFusion in multimodal imaging. Therefore, through multimodal information fusion, the network provides a new technical means for clinical application in noninvasive and precise stroke diagnosis.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"36 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147615231","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}