International Journal of Imaging Systems and Technology最新文献_第3页

TDGU-Net: A Hybrid CNN-Transformer Model for Intracranial Aneurysm Segmentation TDGU-Net：用于颅内动脉瘤分割的CNN-Transformer混合模型

IF 3 4区计算机科学

International Journal of Imaging Systems and Technology Pub Date : 2025-07-09 DOI: 10.1002/ima.70157

Xiaoqing Lin, Chen Wang, Zhengkui Chen, Jianwei Pan, Jijun Tong

{"title":"TDGU-Net: A Hybrid CNN-Transformer Model for Intracranial Aneurysm Segmentation","authors":"Xiaoqing Lin, Chen Wang, Zhengkui Chen, Jianwei Pan, Jijun Tong","doi":"10.1002/ima.70157","DOIUrl":"https://doi.org/10.1002/ima.70157","url":null,"abstract":"<div>\u0000 \u0000 <p>Intracranial aneurysms are life-threatening cerebrovascular conditions, and their accurate identification is crucial for early diagnosis and treatment planning. Automated segmentation technology plays a key role in enhancing diagnostic accuracy and enabling timely intervention. However, the segmentation task is challenging due to the diverse morphologies of aneurysms, indistinct boundaries, and their resemblance to adjacent vascular structures. This study introduces TDGU-Net, a deep learning-based method that combines Convolutional Neural Networks (CNNs) with Transformer architecture to improve segmentation accuracy and efficiency. The model uses CNNs for efficient local feature extraction, while Transformer blocks are employed to establish global relationships within local regions, enhancing the model's ability to capture contextual dependencies. Furthermore, a multi-scale feature fusion module is incorporated to capture critical information across different resolutions, and the Attention Gate mechanism is used to improve the model's ability to accurately identify aneurysm regions. The proposed model was evaluated on the Large IA Segmentation dataset and further validated on the MICCAI 2020 ADAM dataset to demonstrate its adaptability to different datasets. It achieved a Dice coefficient of 76.92% and a sensitivity of 79.65%, demonstrating robust segmentation performance and accurate detection of aneurysms. The proposed method provides a promising tool for the automated diagnosis of intracranial aneurysms, with significant potential for clinical application and improving patient outcomes.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144582381","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}

引用次数: 0

A Novel Framework for Lung Disease Classification Using Multiscale Convolutional Neural Networks With an Integrated Dynamic Attention Mechanism 基于集成动态注意机制的多尺度卷积神经网络的肺部疾病分类新框架

IF 3 4区计算机科学

International Journal of Imaging Systems and Technology Pub Date : 2025-07-08 DOI: 10.1002/ima.70155

Vivekanand Thakare, Shailendra S. Aote, Abhijeet Raipurkar

{"title":"A Novel Framework for Lung Disease Classification Using Multiscale Convolutional Neural Networks With an Integrated Dynamic Attention Mechanism","authors":"Vivekanand Thakare, Shailendra S. Aote, Abhijeet Raipurkar","doi":"10.1002/ima.70155","DOIUrl":"https://doi.org/10.1002/ima.70155","url":null,"abstract":"<div>\u0000 \u0000 <p>Lung disease diagnosis remains a significant clinical challenge due to the similarity in radiological features across various conditions such as COPD, pneumonia, tuberculosis, COVID-19, and lung cancer. Manual interpretation of chest CT scans is time-consuming and subject to inter-observer variability, particularly in resource-limited settings. To address these challenges, this study proposes a novel deep learning framework Multiscale Convolutional Neural Networks with Attention Mechanism (MCNN-AM) for automated classification of lung diseases into six categories, including normal lungs. The model leverages multiscale convolutional layers to extract both localized and global features, enabling better discrimination between diseases with overlapping characteristics. A dynamic attention mechanism, comprising both spatial and channel attention modules, is integrated to emphasize disease-relevant regions and suppress background noise, enhancing the model's diagnostic focus. Additionally, depthwise separable convolutions are utilized to reduce computational complexity while preserving feature richness. The MCNN-AM model is trained and evaluated on publicly available datasets, comprising 6000 training images and 1200 testing images equally distributed across all classes. The model achieves a classification accuracy of 96.84%, outperforming state-of-the-art models such as ResNet50, DenseNet121, and InceptionV3 in terms of precision, recall, F1-score, sensitivity, and specificity. Ablation studies further validate the critical role of the attention modules in achieving high performance. These results demonstrate the potential of MCNN-AM as a reliable, scalable tool for computer-aided diagnosis of lung diseases.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144573892","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}

引用次数: 0

Comparative Assessment of CNN and Transformer U-Nets in Multiple Sclerosis Lesion Segmentation CNN与Transformer U-Nets在多发性硬化症病灶分割中的比较评价

IF 3 4区计算机科学

International Journal of Imaging Systems and Technology Pub Date : 2025-07-07 DOI: 10.1002/ima.70146

Beytullah Sarica, Yunus Serhat Bicakci, Dursun Zafer Seker

{"title":"Comparative Assessment of CNN and Transformer U-Nets in Multiple Sclerosis Lesion Segmentation","authors":"Beytullah Sarica, Yunus Serhat Bicakci, Dursun Zafer Seker","doi":"10.1002/ima.70146","DOIUrl":"https://doi.org/10.1002/ima.70146","url":null,"abstract":"<div>\u0000 \u0000 <p>Multiple sclerosis (MS) is a chronic autoimmune disease that causes lesions in the central nervous system. Accurate segmentation and quantification of these lesions are essential to monitor disease progression and evaluate treatments. Several architectures are used for such studies, the most popular being U-Net-based models. Therefore, this study compares CNN-based and Transformer-based U-Net architectures for MS lesion segmentation. Six U-Net architectures based on CNN and transformer, namely U-Net, R2U-Net, V-Net, Attention U-Net, TransUNet, and SwinUNet, were trained and evaluated on two MS datasets, ISBI2015 and MSSEG2016. T1-w, T2-w, and FLAIR sequences were jointly used to obtain more detailed features. A hybrid loss function, which involves the addition of focal Tversky and Dice losses, was exploited to improve the performance of models. This study was carried out in three steps. First, each model was trained separately and evaluated in each dataset. Second, each model was trained on the ISBI2015 dataset and evaluated on the MSSEG2016 dataset and vice versa. Finally, these two datasets were combined to increase the training samples and assessed on the ISBI2015 dataset. Accordingly, the R2U-Net and the V-Net models (CNN-based) achieved the best ISBI scores among the other models. The R2U-Net model achieved the best ISBI scores in the first and last steps with average scores of 92.82 and 92.91, while the V-Net model achieved the best ISBI score in the second step with an average score of 91.28. Our results show that CNN-based models surpass the Transformer-based U-Net models in most metrics for MS lesion segmentation.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144573525","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}

引用次数: 0

Advancing Computer-Assisted Diabetic Retinopathy Grading: A Super Learner Ensemble Technique for Fundus Imagery 先进的计算机辅助糖尿病视网膜病变分级：眼底图像的超级学习者集成技术

IF 3 4区计算机科学

International Journal of Imaging Systems and Technology Pub Date : 2025-07-07 DOI: 10.1002/ima.70152

Mili Rosline Mathews, S. M. Anzar

{"title":"Advancing Computer-Assisted Diabetic Retinopathy Grading: A Super Learner Ensemble Technique for Fundus Imagery","authors":"Mili Rosline Mathews, S. M. Anzar","doi":"10.1002/ima.70152","DOIUrl":"https://doi.org/10.1002/ima.70152","url":null,"abstract":"<div>\u0000 \u0000 <p>Diabetic retinopathy (DR) is a severe complication of diabetes mellitus and is a predominant global cause of blindness. The accuracy of DR grading is of paramount importance to enable timely and appropriate clinical interventions. This study presents an innovative and comprehensive approach to DR grading that combines convolutional neural networks with an ensemble of diverse machine learning algorithms, referred to as a super learner ensemble. Our methodology includes a preprocessing pipeline designed to enhance the quality of the fundus images in the dataset. To further refine DR grading, we introduce a novel feature extraction model named “RetinaXtract” in conjunction with advanced machine learning classifiers. Statistical analysis tools, specifically the Friedman and Nemenyi tests, are employed to identify the most effective machine learning algorithms. Subsequently, a super learner ensemble is devised by integrating the predictions of the highest-performing machine learning algorithms. This ensemble approach captures a wide range of patterns, thereby enhancing the system's ability to accurately distinguish between different DR stages. Notably, accuracy rates of 99.64%, 99.51%, and 99.16% are achieved on the IDRiD, Kaggle, and Messidor datasets, respectively. This research represents a significant contribution to the field of DR grading, offering a balanced, efficient, and precise classification solution. The introduced methodology has demonstrated substantial promise and holds significant potential for practical applications in the detection and grading of DR from fundus images, ultimately leading to improved clinical outcomes in ophthalmology.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144573704","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}

引用次数: 0

A New Adaptive Sliding Window Method for fMRI Dynamic Functional Connectivity Analysis 一种新的自适应滑动窗口方法用于fMRI动态功能连接分析

IF 3 4区计算机科学

International Journal of Imaging Systems and Technology Pub Date : 2025-07-05 DOI: 10.1002/ima.70154

Ningfei Jiang, Yuhu Shi

{"title":"A New Adaptive Sliding Window Method for fMRI Dynamic Functional Connectivity Analysis","authors":"Ningfei Jiang, Yuhu Shi","doi":"10.1002/ima.70154","DOIUrl":"https://doi.org/10.1002/ima.70154","url":null,"abstract":"<div>\u0000 \u0000 <p>The fixed-window sliding time window method is widely used in exploring dynamics functional connectivity of functional magnetic resonance imaging data analysis, but it is difficult to select a suitable window to capture the dynamic changes in brain function. Therefore, a local polynomial regression (LPR) method is proposed to fit the region of interest (ROI) time series in this paper, in which observations are locally modeled by a least-squares polynomial with a kernel of a certain bandwidth that allows for better bias-variance tradeoff. It combines a data-driven variable bandwidth selection mechanism with intersection of confidence intervals (ICI) and a bandwidth optimization algorithm of particle swarm optimization (PSO). Among them, ICI is used to adaptively determine the locally optimal bandwidth that minimizes the mean square error (MSE), and then the bandwidth values at various time points within all ROIs are computed for each subject. Subsequently, the averaged bandwidth values at these time points is regarded as the bandwidth value for that subject at each time point, followed by generating a time-varying bandwidth sequence for each subject, which is used in the PSO-based bandwidth optimization algorithm. Finally, the results of experiments conducted on simulated data showed that the LPR–ICI–PSO method exhibited lower MSE values on time-varying correlation coefficient estimation for different noise scenarios. Furthermore, we applied the proposed method to the autism spectrum disorder (ASD) study, and obtained a classification accuracy of 74.1% from typical controls (TC) through support vector machine (SVM) with the 10-fold cross-validation strategy. These results demonstrated that our proposed method can effectively capture the dynamic changes in brain function, which is valid in clinical diagnosis and helps to reveal the differences in brain functional connectivity patterns.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144558207","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}

引用次数: 0

Enhancing Brain Tumor Classification Through Optimal Kernel Selection With GL 1 $$ {GL}_1 $$ -Regularization 通过GL 1优化核选择增强脑肿瘤分类$$ {GL}_1 $$ -正则化

IF 3 4区计算机科学

International Journal of Imaging Systems and Technology Pub Date : 2025-07-04 DOI: 10.1002/ima.70150

Otmane Mallouk, Nour-Eddine Joudar, Mohamed Ettaouil

{"title":"Enhancing Brain Tumor Classification Through Optimal Kernel Selection With \u0000 \u0000 \u0000 \u0000 GL\u0000 1\u0000 \u0000 \u0000 $$ {GL}_1 $$\u0000 -Regularization","authors":"Otmane Mallouk, Nour-Eddine Joudar, Mohamed Ettaouil","doi":"10.1002/ima.70150","DOIUrl":"https://doi.org/10.1002/ima.70150","url":null,"abstract":"<div>\u0000 \u0000 <p>Brain tumors, known for their rapid and aggressive growth, are among the most serious and life-threatening diseases worldwide. This makes the development of automated detection methods essential for saving lives. Deep transfer learning has become a highly effective approach for automating brain tumor classification and medical imaging, offering promising solutions on a global scale. However, leveraging a pretrained model typically involves special adaptations. Existing adaptation methods involve freezing or fine-tuning specific layers without considering the contribution level of individual kernels. This work aims to extend the concept of layer-level contributions to the kernel level by employing an adaptive optimization model. Indeed, this paper presents a novel optimization model that incorporates group lasso regularization to control which kernels are frozen and which are fine-tuned. The proposed model selects optimal source features that contribute to the target task. Additionally, the proposed optimization model is solved utilizing proximal gradient descent. The method was evaluated on a three-class brain tumor classification task, distinguishing between glioma, meningioma, and pituitary tumors, using a medical MRI dataset. Several experiments confirm the efficacy of our model in identifying both frozen and fine-tuned kernels, thereby improving data classification. Subsequently, the results obtained are compared with those of state-of-the-art transfer learning methods for comprehensive comparison.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550970","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}

引用次数: 0

Carotid Artery Plague Segmentation Model Based on Dual-Modal 基于双模态的颈动脉鼠疫分割模型

IF 3 4区计算机科学

International Journal of Imaging Systems and Technology Pub Date : 2025-06-30 DOI: 10.1002/ima.70149

Chun He, Zhanquan Sun, Man Chen, Yunqian Huang

{"title":"Carotid Artery Plague Segmentation Model Based on Dual-Modal","authors":"Chun He, Zhanquan Sun, Man Chen, Yunqian Huang","doi":"10.1002/ima.70149","DOIUrl":"https://doi.org/10.1002/ima.70149","url":null,"abstract":"<div>\u0000 \u0000 <p>Ultrasonography (US) and contrast-enhanced ultrasound (CEUS) are effective imaging tools for analyzing the spatial and temporal characteristics of lesions and diagnosing or predicting diseases. At the same time, US is characterized by blurred boundaries and strong noise interference. Therefore, evaluating plaques and depicting lesions frame-by-frame is a time-consuming task, which poses a challenge in analyzing US videos using deep learning techniques. However, despite the existing methods for US and CEUS image segmentation, there are still limited approaches capable of integrating the feature information from these two distinct image types. Furthermore, these methods require additional optimization to enhance their capacity for extracting comprehensive global contextual information. To address the problem, we propose a U-shaped structured network model based on Transformer in this paper. The network is composed of two parts, that is, the dual-modal information interaction fusion module and the enhanced feature extraction module. The first module is used to extract comprehensive US and CEUS features and fuse them at multiple scales. The second module is used to enhance feature extraction capabilities. This network enables precise localization of the lesion and clear depiction of the region of interest in US. Our model achieved a Dice of 91.62% and an IoU of 88.04% on the carotid plaque segmentation dataset. The experimental results show that the performance of our designed network on the carotid artery dataset is better than that of the SOTA models.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144515005","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}

引用次数: 0

BFR-Unet: A Full-Resolution Model for Efficient Segmentation of Tiny Blood Vessels BFR-Unet：一种有效分割微小血管的全分辨率模型

IF 3 4区计算机科学

International Journal of Imaging Systems and Technology Pub Date : 2025-06-27 DOI: 10.1002/ima.70148

Feng Liu, Jipeng Sun

{"title":"BFR-Unet: A Full-Resolution Model for Efficient Segmentation of Tiny Blood Vessels","authors":"Feng Liu, Jipeng Sun","doi":"10.1002/ima.70148","DOIUrl":"https://doi.org/10.1002/ima.70148","url":null,"abstract":"<div>\u0000 \u0000 <p>Retinal blood vessel segmentation plays a crucial role in diagnosing retinal diseases, where accurate and complete vessel segmentation is essential for reliable diagnosis. Currently, U-Net remains one of the most widely used architectures for retinal blood vessel segmentation. However, due to the complexity and variability of retinal structures, the blood vessel edges are often very thin, and the low contrast of retinal images further complicates accurate segmentation. These challenges frequently result in U-Net models failing to precisely capture vessel boundaries. To address this issue, a novel full-resolution retinal blood vessel segmentation network, termed BFR-Net, is proposed. The BFR-Net is composed of three primary modules: the multi-residual convolution module, the boundary attention module, and the feature fusion module. The multi-residual convolution module, forming the backbone of the network, enables effective extraction of contextual information across the full resolution. The boundary attention module processes outputs from both the backbone and different network levels to capture detailed edge features, thus enhancing the segmentation performance. Finally, the feature fusion module integrates features from the backbone and boundary attention modules, further improving overall network performance. The performance of the proposed model is evaluated on three commonly used retinal vessel segmentation datasets. Experimental results demonstrate that BFR-Net achieves advanced performance, particularly in segmenting vessel edges and small blood vessels. Specifically, on the DRIVE and CHSAE_DB1 datasets, the Se and F1 scores are 0.8646, 0.8244, 0.8838, and 0.8108, respectively. These results demonstrate that the proposed network exhibits excellent performance in segmenting vessel boundaries and fine vessels.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492789","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}

引用次数: 0

Improving Stroke Segmentation and Classification Performance Using a Goal-Oriented Deep Learning Framework 使用面向目标的深度学习框架改进笔画分割和分类性能

IF 3 4区计算机科学

International Journal of Imaging Systems and Technology Pub Date : 2025-06-27 DOI: 10.1002/ima.70147

Büşra Uygun, Ayşe Demirhan

{"title":"Improving Stroke Segmentation and Classification Performance Using a Goal-Oriented Deep Learning Framework","authors":"Büşra Uygun, Ayşe Demirhan","doi":"10.1002/ima.70147","DOIUrl":"https://doi.org/10.1002/ima.70147","url":null,"abstract":"<div>\u0000 \u0000 <p>CT scans play a crucial role in diagnosing and planning treatment for strokes, offering essential insights into the location, size, and extent of bleeding in brain tissue. This study explores two distinct scenarios for stroke detection, classification, and segmentation, utilizing 6951 brain CT images from the TEKNOFEST competition. In both scenarios, CT images undergo preprocessing steps involving skull-stripping, normalization, and image augmentation. In the first scenario, stroke presence-absence classification achieved a 98% success rate on test images. Subsequent segmentation of images with strokes resulted in Dice scores of 59% for ischemic stroke and 67% for hemorrhagic stroke on test images. The classification of stroke types as ischemic and hemorrhagic achieved a 100% success rate, with a 97% success rate when directly classifying stroke types in images without segmentation. This indicates a 3% performance improvement when applying the classification process after stroke region segmentation. In the second scenario, a three-class classification of no stroke, ischemic stroke, and hemorrhagic stroke achieved an average of 97% success on test images. Post-classification, separately created models for the segmentation of ischemic and hemorrhagic strokes yielded Dice scores of 78% and 79%, respectively. The second scenario demonstrated a performance improvement of 19% and 12% for the segmentation of ischemic and hemorrhagic strokes through the post-classification segmentation process. The proposed approach outperforms competing teams in the competition rankings.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144493001","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}

引用次数: 0

Research on Lung Sound Signal Image Feature Recognition Based on Temporal and Spatial Dual-Channel Long- and Short-Term Memory Model 基于时空双通道长短期记忆模型的肺声信号图像特征识别研究

IF 3 4区计算机科学

International Journal of Imaging Systems and Technology Pub Date : 2025-06-23 DOI: 10.1002/ima.70141

Li Xueri, Hu Ruo, Xu Hong, Zhao Huimin

{"title":"Research on Lung Sound Signal Image Feature Recognition Based on Temporal and Spatial Dual-Channel Long- and Short-Term Memory Model","authors":"Li Xueri, Hu Ruo, Xu Hong, Zhao Huimin","doi":"10.1002/ima.70141","DOIUrl":"https://doi.org/10.1002/ima.70141","url":null,"abstract":"<p>In this paper, through the study on the transformation of lung sound signal into image feature signal processing, we further mastered the processing process of lung sound signal, and used the new neural network model to identify and diagnose the image features of lung sound, effectively improving the effect of clinical AI-assisted diagnosis. To solve the problem that the traditional neural network model cannot obtain the temporal and spatial characteristics of lung sound signals at the same time, we propose a DCCLSTM (Dual-Channel Convolutional neural network for Long- and Short-Time Memory) to obtain spatial information and temporal information features of lung sound simultaneously. New features are generated by weighted fusion, which can effectively make up for the problem that the resolution of the feature map extracted by the traditional neural network model is reduced. This report presents the results of studies conducted on the lung sound dataset, and the accuracy rate of Dalal_CNN with the best effect was 89.56%. The DCCLSTM proposed in this study has a recognition accuracy of 97.40%. Experiments show that the DCCLSTM method is more accurate than the Dalal_CNN method.</p>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ima.70141","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144339541","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}

引用次数: 0