Journal of X-Ray Science and Technology最新文献

{"title":"Proximal femur segmentation and quantification in dual-energy subtraction tomosynthesis: A novel approach to fracture risk assessment.","authors":"Akari Matsushima, Tai-Been Chen, Koharu Kimura, Mizuki Sato, Shih-Yen Hsu, Takahide Okamoto","doi":"10.1177/08953996241312594","DOIUrl":"https://doi.org/10.1177/08953996241312594","url":null,"abstract":"<p><strong>Background: </strong>Osteoporosis is a major public health concern, especially among older adults, due to its association with an increased risk of fractures, particularly in the proximal femur. These fractures severely impact mobility and quality of life, leading to significant economic and health burdens.</p><p><strong>Objective: </strong>This study aims to enhance bone density assessment in the proximal femur by addressing the limitations of conventional dual-energy X-ray absorptiometry through the integration of tomosynthesis with dual-energy applications and advanced segmentation models.</p><p><strong>Methods and materials: </strong>The imaging capability of a radiography/fluoroscopy system with dual-energy subtraction was evaluated. Two phantoms were included in this study: a tomosynthesis phantom (PH-56) was used to measure the quality of the tomosynthesis images, and a torso phantom (PH-4) was used to obtain proximal femur images. Quantification of bone images was achieved by optimizing the energy subtraction (ene-sub) and scale factors to isolate bone pixel values while nullifying soft tissue pixel values. Both the faster region-based convolutional neural network (Faster R-CNN) and U-Net were used to segment the proximal femoral region. The performance of these models was then evaluated using the intersection-over-union (IoU) metric with a torso phantom to ensure controlled conditions.</p><p><strong>Results: </strong>The optimal ene-sub-factor ranged between 1.19 and 1.20, and a scale factor of around 0.1 was found to be suitable for detailed bone image observation. Regarding segmentation performance, a VGG19-based Faster R-CNN model achieved the highest mean IoU, outperforming the U-Net model (0.865 vs. 0.515, respectively).</p><p><strong>Conclusions: </strong>These findings suggest that the integration of tomosynthesis with dual-energy applications significantly enhances the accuracy of bone density measurements in the proximal femur, and that the Faster R-CNN model provides superior segmentation performance, thereby offering a promising tool for bone density and osteoporosis management. Future research should focus on refining these models and validating their clinical applicability to improve patient outcomes.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"8953996241312594"},"PeriodicalIF":1.7,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing lung cancer diagnosis: Combining 3D auto-encoders and attention mechanisms for CT scan analysis.","authors":"Meng Wang, Zi Yang, Ruifeng Zhao","doi":"10.1177/08953996241313120","DOIUrl":"https://doi.org/10.1177/08953996241313120","url":null,"abstract":"<p><strong>Objective: </strong>The goal of this study is to assess the effectiveness of a hybrid deep learning model that combines 3D Auto-encoders with attention mechanisms to detect lung cancer early from CT scan images. The study aims to improve diagnostic accuracy, sensitivity, and specificity by focusing on key features in the scans.</p><p><strong>Materials and methods: </strong>A hybrid model was developed that combines feature extraction using 3D Auto-encoder networks with attention mechanisms. First, the 3D Auto-encoder model was tested without attention, using feature selection techniques such as RFE, LASSO, and ANOVA. This was followed by evaluation using several classifiers: SVM, RF, GBM, MLP, LightGBM, XGBoost, Stacking, and Voting. The model's performance was evaluated based on accuracy, sensitivity, F1-Score, and AUC-ROC. After that, attention mechanisms were added to help the model focus on important areas in the CT scans, and the performance was re-assessed.</p><p><strong>Results: </strong>The 3D Auto-encoder model without attention achieved an accuracy of 93% and sensitivity of 89%. When attention mechanisms were added, the performance improved across all metrics. For example, the accuracy of SVM increased to 94%, sensitivity to 91%, and AUC-ROC to 0.96. Random Forest (RF) also showed improvements, with accuracy rising to 94% and AUC-ROC to 0.93. The final model with attention improved the overall accuracy to 93.4%, sensitivity to 90.2%, and AUC-ROC to 94.1%. These results highlight the important role of attention in identifying the most relevant features for accurate classification.</p><p><strong>Conclusions: </strong>The proposed hybrid deep learning model, especially with the addition of attention mechanisms, significantly improves the early detection of lung cancer. By focusing on key features in the CT scans, the attention mechanism helps reduce false negatives and boosts overall diagnostic accuracy. This approach has great potential for use in clinical applications, particularly in the early-stage detection of lung cancer.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"8953996241313120"},"PeriodicalIF":1.7,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Study on the influence of square fiber diameter quality on the optical characteristics of lobster eye X-ray micro pore optics.","authors":"Yang Cao, Honggang Wang, Yanan Wang, Longhui Li, Yunsheng Qian, Yizheng Lang","doi":"10.1177/08953996241306697","DOIUrl":"https://doi.org/10.1177/08953996241306697","url":null,"abstract":"<p><strong>Background: </strong>The lobster eye micro pore optics (MPO) plays a pivotal role in X-ray focusing, composed of thousands of hollow square microfibers. The channel error in MPO can profoundly impact its focusing performance. Due to the complex manufacturing process of MPO, there are numerous factors that can contribute to channel errors.</p><p><strong>Objective: </strong>This paper investigates the impact of two key quality indicators of fiber, i.e., diameter precision and ovality, on the focusing performance of flat MPO.</p><p><strong>Methods: </strong>During the actual production process of MPO, fibers with varying diameter precision and ovality are utilized, and point-to-point vacuum X-ray focusing equipment is used to assess MPO's focusing performance. Channel error models related to fiber diameter accuracy and ovality are established in the simulation.</p><p><strong>Results: </strong>Experiments show that both the diameter precision and ovality of fiber influence MPO focusing abilities, with diameter precision primarily affecting the intensity and uniformity of the central point focus and the parallelism of the line foci, while ovality mainly affects the intensity and continuity of the line foci. Numerical simulation results reveal that tilt channel errors significantly affect the X-ray focusing effects.</p><p><strong>Conclusions: </strong>These findings hold important guiding significance for the preparation process of square fibers and high quality X-ray focusing device.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"8953996241306697"},"PeriodicalIF":1.7,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on meshing method for industrial CT volume data based on iterative smooth signed distance surface reconstruction.","authors":"ShiBo Jiang, Shuo Xu, YueWen Sun, ZhiFang Wu","doi":"10.1177/08953996241306691","DOIUrl":"https://doi.org/10.1177/08953996241306691","url":null,"abstract":"<p><p>Industrial Computed Tomography (CT) technology is increasingly applied in fields such as additive manufacturing and non-destructive testing, providing rich three-dimensional information for various fields, which is crucial for internal structure detection, defect detection, and product development. In subsequent processes such as analysis, simulation, and editing, three-dimensional volume data models often need to be converted into mesh models, making effective meshing of volume data essential for expanding the application scenarios and scope of industrial CT. However, the existing Marching Cubes algorithm has issues with low efficiency and poor mesh quality during the volume data meshing process. To overcome these limitations, this study proposes an innovative method for industrial CT volume data meshing based on the Iterative Smooth Signed Surface Distance (iSSD) algorithm. This method first refines the segmented voxel model, accurately extracts boundary voxels, and constructs a high-quality point cloud model. By randomly initializing the normals of the point cloud and iteratively updating the point cloud normals, the mesh is reconstructed using the SSD algorithm after each iteration update, ultimately achieving high-quality, watertight, and smooth mesh model reconstruction, ensuring the accuracy and reliability of the reconstructed mesh. Qualitative and quantitative analyses with other methods have further highlighted the excellent performance of the method proposed in this paper. This study not only improves the efficiency and quality of volume data meshing but also provides a solid foundation for subsequent three-dimensional analysis, simulation, and editing, and has important industrial application prospects and academic value.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"8953996241306691"},"PeriodicalIF":1.7,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Orthogonal limited-angle CT reconstruction method based on anisotropic self-guided image filtering.","authors":"Gong Changcheng, Song Qiang","doi":"10.1177/08953996241300013","DOIUrl":"https://doi.org/10.1177/08953996241300013","url":null,"abstract":"<p><p>Computed tomography (CT) reconstruction from incomplete projection data is significant for reducing radiation dose or scanning time. In this work, we investigate a special sampling strategy, which performs two limited-angle scans. We call it orthogonal limited-angle sampling. The X-ray source trajectory covers two limited-angle ranges, and the angle bisectors of the two angular ranges are orthogonal. This sampling method avoids rapid switching of tube voltage in few-view sampling, and reduces data correlation of projections in limited-angle sampling. It has the potential to become a practical imaging strategy. Then we propose a new reconstruction model based on anisotropic self-guided image filtering (ASGIF) and present an algorithm to solve this model. We construct adaptive weights to guide image reconstruction using the gradient information of reconstructed image itself. Additionally, since the shading artifacts are related to the scanning angular ranges and distributed in two orthogonal directions, anisotropic image filtering is used to preserve image edges. Experiments on a digital phantom and real CT data demonstrate that ASGIF method can effectively suppress shading artifacts and preserve image edges, outperforming other competing methods.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"8953996241300013"},"PeriodicalIF":1.7,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multi-model machine learning framework for breast cancer risk stratification using clinical and imaging data.","authors":"Lu Miao, Zidong Li, Jinnan Gao","doi":"10.1177/08953996241308175","DOIUrl":"https://doi.org/10.1177/08953996241308175","url":null,"abstract":"<p><strong>Purpose: </strong>This study presents a comprehensive machine learning framework for assessing breast cancer malignancy by integrating clinical features with imaging features derived from deep learning.</p><p><strong>Methods: </strong>The dataset included 1668 patients with documented breast lesions, incorporating clinical data (e.g., age, BI-RADS category, lesion size, margins, and calcifications) alongside mammographic images processed using four CNN architectures: EfficientNet, ResNet, DenseNet, and InceptionNet. Three predictive configurations were developed: an imaging-only model, a hybrid model combining imaging and clinical data, and a stacking-based ensemble model that aggregates both data types to enhance predictive accuracy. Twelve feature selection techniques, including ReliefF and Fisher Score, were applied to identify key predictive features. Model performance was evaluated using accuracy and AUC, with 5-fold cross-valida tion and hyperparameter tuning to ensure robustness.</p><p><strong>Results: </strong>The imaging-only models demonstrated strong predictive performance, with EfficientNet achieving an AUC of 0.76. The hybrid model combining imaging and clinical data reached the highest accuracy of 83% and an AUC of 0.87, underscoring the benefits of data integration. The stacking-based ensemble model further optimized accuracy, reaching a peak AUC of 0.94, demonstrating its potential as a reliable tool for malignancy risk assessment.</p><p><strong>Conclusion: </strong>This study highlights the importance of integrating clinical and deep imaging features for breast cancer risk stratification, with the stacking-based model.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"8953996241308175"},"PeriodicalIF":1.7,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on ring artifact reduction method for CT images of nuclear graphite components.","authors":"Tianchen Zeng, Jintao Fu, Peng Cong, Ximing Liu, Guangduo Xu, Yuewen Sun","doi":"10.1177/08953996241308760","DOIUrl":"https://doi.org/10.1177/08953996241308760","url":null,"abstract":"<p><strong>Background: </strong>The supporting structure of high-temperature gas-cooled reactors (HTGR) comprises over 3000 carbon/graphite components, necessitating computed tomography (CT) non-destructive testing before operational deployment as per reactor technical specifications. However, CT images are frequently marred by severe ring artifacts due to the response non-uniformity and non-linearity of detector units, which diminishes the ability to detect defects effectively.</p><p><strong>Methods: </strong>To address this issue, we propose a physics-based ring artifacts reduction method for CT that employs pixel response correction. This method physically accounts for the cause of ring artifacts and leverages the prior knowledge of the detected object to enhance the accuracy of the detection process.</p><p><strong>Results: </strong>Our proposed method achieved a notable reduction in ring artifacts, as evidenced by a 37.7% decrease in ring total variation (RTV) values compared to the originals, significantly enhancing image quality. It also surpassed traditional and machine learning methods in artifact reduction while maintaining image details. The lower RTV scores confirm our method's superior effectiveness in minimizing ring artifacts.</p><p><strong>Conclusion: </strong>We believe that our research contributes to the enhancement of defect inspection performance in detection systems, which is crucial for ensuring the safety of reactors. The proposed method's effectiveness in mitigating ring artifacts while maintaining image quality highlights its potential impact on the reliability of non-destructive testing in the context of HTGR components.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"8953996241308760"},"PeriodicalIF":1.7,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploiting commercial micro X-ray fluorescence systems for stereoscopic soft X-ray imaging.","authors":"Ricardo Baettig, Ben Ingram, Ricardo A Cabeza","doi":"10.1177/08953996241291356","DOIUrl":"https://doi.org/10.1177/08953996241291356","url":null,"abstract":"<p><strong>Background: </strong>Commercial micro X-ray fluorescence (μXRF) systems often employ a tilted convergent beam, which can cause a misalignment between X-ray cartography and the corresponding visible images. This misalignment is typically considered a disadvantage, as it hinders the accurate spatial correlation of elemental information. However, this apparent drawback can be exploited to facilitate X-ray stereoscopy.</p><p><strong>Objective: </strong>To demonstrate the use of unmodified commercial μXRF equipment to estimate the 3D configurations of metals and voids within a low-atomic-weight matrix, specifically polymethyl methacrylate, and to explore the implications for enhancing μXRF mapping techniques. This approach could have applications in materials science, archaeology, and other fields requiring non-destructive 3D chemical mapping.</p><p><strong>Methods: </strong>Using unmodified commercial μXRF equipment, we leveraged both XRF and Compton scattering effects to quantitatively reconstruct the size, position, and depth of embedded tungsten, copper, and silver objects. The study specifically examines how beam divergence affects the acutance of objects located deeper within the sample.</p><p><strong>Results: </strong>Our findings indicate a depth estimation bias ranging from 4% to 15% for depths between 24 mm, and a size estimation bias below 3.2%. These results validate the methodology and highlight the robustness of our approach under typical operational settings, suggesting that the technique could be applied to a wide range of samples with minimal modifications to existing μXRF systems.</p><p><strong>Conclusions: </strong>The study confirms that the inclination-induced misalignment in μXRF systems can be harnessed to enhance three-dimensional imaging capabilities. Our work establishes a foundation for advancing current μXRF mapping techniques and interpretation strategies, potentially broadening the applications of μXRF in various scientific and industrial fields.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"8953996241291356"},"PeriodicalIF":1.7,"publicationDate":"2025-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An effective COVID-19 classification in X-ray images using a new deep learning framework.","authors":"P Thilagavathi, R Geetha, S Jothi Shri, K Somasundaram","doi":"10.1177/08953996241290893","DOIUrl":"https://doi.org/10.1177/08953996241290893","url":null,"abstract":"<p><strong>Background: </strong>The global concern regarding the diagnosis of lung-related diseases has intensified due to the rapid transmission of coronavirus disease 2019 (COVID-19). Artificial Intelligence (AI) based methods are emerging technologies that help to identify COVID-19 in chest X-ray images quickly.</p><p><strong>Method: </strong>In this study, the publically accessible database COVID-19 Chest X-ray is used to diagnose lung-related disorders using a hybrid deep-learning approach. This dataset is pre-processed using an Improved Anisotropic Diffusion Filtering (IADF) method. After that, the features extraction methods named Grey-level Co-occurrence Matrix (GLCM), uniform Local Binary Pattern (uLBP), Histogram of Gradients (HoG), and Horizontal-vertical neighbourhood local binary pattern (hvnLBP) are utilized to extract the useful features from the pre-processed dataset. The dimensionality of a feature set is subsequently reduced through the utilization of an Adaptive Reptile Search Optimization (ARSO) algorithm, which optimally selects the features for flawless classification. Finally, the hybrid deep learning algorithm, Multi-head Attention-based Bi-directional Gated Recurrent unit with Deep Sparse Auto-encoder Network (MhA-Bi-GRU with DSAN), is developed to perform the multiclass classification problem. Moreover, a Dynamic Levy-Flight Chimp Optimization (DLF-CO) algorithm is applied to minimize the loss function in the hybrid algorithm.</p><p><strong>Results: </strong>The whole simulation is performed using the Python language in which the 0.001 learning rate accomplishes the proposed method's higher classification accuracy of 0.95%, and 0.98% is obtained for a 0.0001 learning rate. Overall, the performance of the proposed methodology outperforms all existing methods employing different performance parameters.</p><p><strong>Conclusion: </strong>The proposed hybrid deep-learning approach with various feature extraction, and optimal feature selection effectively diagnoses disease using Chest X-ray images demonstrated through classification accuracy.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"8953996241290893"},"PeriodicalIF":1.7,"publicationDate":"2025-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DML-MFCM: A multimodal fine-grained classification model based on deep metric learning for Alzheimer's disease diagnosis.","authors":"Heng Wang, Tiejun Yang, Jiacheng Fan, Huiyao Zhang, Wenjie Zhang, Mingzhu Ji, Jianyu Miao","doi":"10.1177/08953996241300023","DOIUrl":"10.1177/08953996241300023","url":null,"abstract":"<p><strong>Background: </strong>Alzheimer's disease (AD) is a neurodegenerative disorder. There are no drugs and methods for the treatment of AD, but early intervention can delay the deterioration of the disease. Therefore, the early diagnosis of AD and mild cognitive impairment (MCI) is significant. Structural magnetic resonance imaging (sMRI) is widely used to present structural changes in the subject's brain tissue. The relatively mild structural changes in the brain with MCI have led to ongoing challenges in the task of conversion prediction in MCI. Moreover, many multimodal AD diagnostic models proposed in recent years ignore the potential relationship between multimodal information.</p><p><strong>Objective: </strong>To solve these problems, we propose a multimodal fine-grained classification model based on deep metric learning for AD diagnosis (DML-MFCM), which can fully exploit the fine-grained feature information of sMRI and learn the potential relationships between multimodal feature information.</p><p><strong>Methods: </strong>First, we propose a fine-grained feature extraction module that can effectively capture the fine-grained feature information of the lesion area. Then, we introduce a multimodal cross-attention module to learn the potential relationships between multimodal data. In addition, we design a hybrid loss function based on deep metric learning. It can guide the model to learn the feature representation method between samples, which improves the model's performance in disease diagnosis.</p><p><strong>Results: </strong>We have extensively evaluated the proposed models on the ADNI and AIBL datasets. The ACC of AD vs. NC, MCI vs. NC, and sMCI vs. pMCI tasks in the ADNI dataset are 98.75%, 95.88%, and 88.00%, respectively. The ACC on the AD vs. NC and MCI vs. NC tasks in the AIBL dataset are 94.33% and 91.67%.</p><p><strong>Conclusions: </strong>The results demonstrate that our method has excellent performance in AD diagnosis.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"211-228"},"PeriodicalIF":1.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}