Journal of X-Ray Science and Technology最新文献

{"title":"Coronary artery segmentation in CCTA images based on multi-scale feature learning.","authors":"Bu Xu, Jinzhong Yang, Peng Hong, Xiaoxue Fan, Yu Sun, Libo Zhang, Benqiang Yang, Lisheng Xu, Alberto Avolio","doi":"10.3233/XST-240093","DOIUrl":"10.3233/XST-240093","url":null,"abstract":"<p><strong>Background: </strong>Coronary artery segmentation is a prerequisite in computer-aided diagnosis of Coronary Artery Disease (CAD). However, segmentation of coronary arteries in Coronary Computed Tomography Angiography (CCTA) images faces several challenges. The current segmentation approaches are unable to effectively address these challenges and existing problems such as the need for manual interaction or low segmentation accuracy.</p><p><strong>Objective: </strong>A Multi-scale Feature Learning and Rectification (MFLR) network is proposed to tackle the challenges and achieve automatic and accurate segmentation of coronary arteries.</p><p><strong>Methods: </strong>The MFLR network introduces a multi-scale feature extraction module in the encoder to effectively capture contextual information under different receptive fields. In the decoder, a feature correction and fusion module is proposed, which employs high-level features containing multi-scale information to correct and guide low-level features, achieving fusion between the two-level features to further improve segmentation performance.</p><p><strong>Results: </strong>The MFLR network achieved the best performance on the dice similarity coefficient, Jaccard index, Recall, F1-score, and 95% Hausdorff distance, for both in-house and public datasets.</p><p><strong>Conclusion: </strong>Experimental results demonstrate the superiority and good generalization ability of the MFLR approach. This study contributes to the accurate diagnosis and treatment of CAD, and it also informs other segmentation applications in medicine.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"973-991"},"PeriodicalIF":1.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141472046","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":"Reference-free calibration method for asynchronous rotation in robotic CT.","authors":"Xuan Zhou, Yuedong Liu, Cunfeng Wei, Qiong Xu","doi":"10.3233/XST-240023","DOIUrl":"10.3233/XST-240023","url":null,"abstract":"<p><strong>Background: </strong>Geometry calibration for robotic CT system is necessary for obtaining acceptable images under the asynchrony of two manipulators.</p><p><strong>Objective: </strong>We aim to evaluate the impact of different types of asynchrony on images and propose a reference-free calibration method based on a simplified geometry model.</p><p><strong>Methods: </strong>We evaluate the impact of different types of asynchrony on images and propose a novel calibration method focused on asynchronous rotation of robotic CT. The proposed method is initialized with reconstructions under default uncalibrated geometry and uses grid sampling of estimated geometry to determine the direction of optimization. Difference between the re-projections of sampling points and the original projection is used to guide the optimization direction. Images and estimated geometry are optimized alternatively in an iteration, and it stops when the difference of residual projections is close enough, or when the maximum iteration number is reached.</p><p><strong>Results: </strong>In our simulation experiments, proposed method shows better performance, with the PSNR increasing by 2%, and the SSIM increasing by 13.6% after calibration. The experiments reveal fewer artifacts and higher image quality.</p><p><strong>Conclusion: </strong>We find that asynchronous rotation has a more significant impact on reconstruction, and the proposed method offers a feasible solution for correcting asynchronous rotation.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"1239-1252"},"PeriodicalIF":1.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141601992","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":"Can AI generate diagnostic reports for radiologist approval on CXR images? A multi-reader and multi-case observer performance study.","authors":"Lin Guo, Li Xia, Qiuting Zheng, Bin Zheng, Stefan Jaeger, Maryellen L Giger, Jordan Fuhrman, Hui Li, Fleming Y M Lure, Hongjun Li, Li Li","doi":"10.3233/XST-240051","DOIUrl":"10.3233/XST-240051","url":null,"abstract":"<p><strong>Background: </strong>Accurately detecting a variety of lung abnormalities from heterogenous chest X-ray (CXR) images and writing radiology reports is often difficult and time-consuming.</p><p><strong>Objective: </strong>To access the utility of a novel artificial intelligence (AI) system (MOM-ClaSeg) in enhancing the accuracy and efficiency of radiologists in detecting heterogenous lung abnormalities through a multi-reader and multi-case (MRMC) observer performance study.</p><p><strong>Methods: </strong>Over 36,000 CXR images were retrospectively collected from 12 hospitals over 4 months and used as the experiment group and the control group. In the control group, a double reading method is used in which two radiologists interpret CXR to generate a final report, while in the experiment group, one radiologist generates the final reports based on AI-generated reports.</p><p><strong>Results: </strong>Compared with double reading, the diagnostic accuracy and sensitivity of single reading with AI increases significantly by 1.49% and 10.95%, respectively (P < 0.001), while the difference in specificity is small (0.22%) and without statistical significance (P = 0.255). Additionally, the average image reading and diagnostic time in the experimental group is reduced by 54.70% (P < 0.001).</p><p><strong>Conclusion: </strong>This MRMC study demonstrates that MOM-ClaSeg can potentially serve as the first reader to generate the initial diagnostic reports, with a radiologist only reviewing and making minor modifications (if needed) to arrive at the final decision. It also shows that single reading with AI can achieve a higher diagnostic accuracy and efficiency than double reading.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"1465-1480"},"PeriodicalIF":1.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142479212","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 the effectiveness of multi-view slice correction strategy based on deep learning in high pitch helical CT reconstruction.","authors":"Zihan Deng, Zhisheng Wang, Legeng Lin, Demin Jiang, Junning Cui, Shunli Wang","doi":"10.3233/XST-240128","DOIUrl":"10.3233/XST-240128","url":null,"abstract":"<p><strong>Background: </strong>Recent studies have explored layered correction strategies, employing a slice-by-slice approach to mitigate the prominent limited-view artifacts present in reconstructed images from high-pitch helical CT scans. However, challenges persist in determining the angles, quantity, and sequencing of slices.</p><p><strong>Objective: </strong>This study aims to explore the optimal slicing method for high pitch helical scanning 3D reconstruction. We investigate the impact of slicing angle, quantity, order, and model on correction effectiveness, aiming to offer valuable insights for the clinical application of deep learning methods.</p><p><strong>Methods: </strong>In this study, we constructed and developed a series of data-driven slice correction strategies for 3D high pitch helical CT images using slice theory, and conducted extensive experiments by adjusting the order, increasing the number, and replacing the model.</p><p><strong>Results: </strong>The experimental results indicate that indiscriminately augmenting the number of correction directions does not significantly enhance the quality of 3D reconstruction. Instead, optimal reconstruction outcomes are attained by aligning the final corrected slice direction with the observation direction.</p><p><strong>Conclusions: </strong>The data-driven slicing correction strategy can effectively solve the problem of artifacts in high pitch helical scanning. Increasing the number of slices does not significantly improve the quality of the reconstruction results, ensuring that the final correction angle is consistent with the observation angle to achieve the best reconstruction quality.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"1535-1552"},"PeriodicalIF":1.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142866165","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":"Multiple energy X-ray imaging of metal oxide particles inside gingival tissues.","authors":"Jarrod Cortez, Ignacio Romero, Jason Ngo, Md Sayed Tanveer Azam, Chuang Niu, Cássio Luiz Coutinho Almeida-da-Silva, Leticia Ferreira Cabido, David M Ojcius, Wei-Chun Chin, Ge Wang, Changqing Li","doi":"10.3233/XST-230175","DOIUrl":"10.3233/XST-230175","url":null,"abstract":"<p><strong>Background: </strong>Periodontal disease affects over 50% of the global population and is characterized by gingivitis as the initial sign. One dental health issue that may contribute to the development of periodontal disease is foreign body gingivitis (FBG), which can result from exposure to some kinds of foreign metal particles from dental products or food.</p><p><strong>Objective: </strong>We design a novel, portable, affordable, multispectral X-ray and fluorescence optical microscopic imaging system dedicated to detecting and differentiating metal oxide particles in dental pathological tissues. A novel denoising algorithm is applied. We verify the feasibility and optimize the performance of the imaging system with numerical simulations.</p><p><strong>Methods: </strong>The designed imaging system has a focused X-ray tube with tunable energy spectra and thin scintillator coupled with an optical microscope as detector. A simulated soft tissue phantom is embedded with 2-micron thick metal oxide discs as the imaged object. GATE software is used to optimize the systematic parameters such as energy bandwidth and X-ray photon number. We have also applied a novel denoising method, Noise2Sim with a two-layer UNet structure, to improve the simulated image quality.</p><p><strong>Results: </strong>The use of an X-ray source operating with an energy bandwidth of 5 keV, X-ray photon number of 108, and an X-ray detector with a 0.5 micrometer pixel size in a 100 by 100-pixel array allowed for the detection of particles as small as 0.5 micrometer. With the Noise2Sim algorithm, the CNR has improved substantially. A typical example is that the Aluminum (Al) target's CNR is improved from 6.78 to 9.72 for the case of 108 X-ray photons with the Chromium (Cr) source of 5 keV bandwidth.</p><p><strong>Conclusions: </strong>Different metal oxide particles were differentiated using Contrast-to-Noise ratio (CNR) by utilizing four different X-ray spectra.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"87-103"},"PeriodicalIF":1.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138048318","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":"Anatomical changes and dosimetric analysis of the neck region based on FBCT for nasopharyngeal carcinoma patients during radiotherapy.","authors":"Aoqiang Chen, Xuemei Chen, Xiaobo Jiang, Yajuan Wang, Feng Chi, Dehuan Xie, Meijuan Zhou","doi":"10.3233/XST-230280","DOIUrl":"10.3233/XST-230280","url":null,"abstract":"<p><strong>Background: </strong>The study aimed to investigate anatomical changes in the neck region and evaluate their impact on dose distribution in patients with nasopharyngeal carcinoma (NPC) undergoing intensity modulated radiation therapy (IMRT). Additionally, the study sought to determine the optimal time for replanning during the course of treatment.</p><p><strong>Methods: </strong>Twenty patients diagnosed with NPC underwent IMRT, with weekly pretreatment kV fan beam computed tomography (FBCT) scans in the treatment room. Metastasized lymph nodes in the neck region and organs at risk (OARs) were redelineation using the images from the FBCT scans. Subsequently, the original treatment plan (PLAN0) was replicated to each FBCT scan to generate new plans labeled as PLAN 1-6. The dose-volume histograms (DVH) of the new plans and the original plan were compared. One-way repeated measure ANOVA was utilized to establish threshold(s) at various time points. The presence of such threshold(s) would signify significant change(s), suggesting the need for replanning.</p><p><strong>Results: </strong>Progressive volume reductions were observed over time in the neck region, the gross target volume for metastatic lymph nodes (GTVnd), as well as the submandibular glands and parotids. Compared to PLAN0, the mean dose (Dmean) of GTVnd-L significantly increased in PLAN5, while the minimum dose covering 95% of the volume (D95%) of PGTVnd-L showed a significant decrease from PLAN3 to PLAN6. Similarly, the Dmean of GTVnd-R significantly increased from PLAN4 to PLAN6, whereas the D95% of PGTVnd-R exhibited a significant decrease during the same period. Furthermore, the dose of bilateral parotid glands, bilateral submandibular glands, brainstem and spinal cord was gradually increased in the middle and late period of treatment.</p><p><strong>Conclusion: </strong>Significant anatomical and dosimetric changes were noted in both the target volumes and OARs. Considering the thresholds identified, it is imperative to undertake replanning at approximately 20 fractions. This measure ensures the delivery of adequate doses to target volumes while mitigating the risk of overdosing on OARs.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"783-795"},"PeriodicalIF":3.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140061028","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":"Artificial intelligence auxiliary diagnosis and treatment system for breast cancer in developing countries.","authors":"Wenxiu Li, Fangfang Gou, Jia Wu","doi":"10.3233/XST-230194","DOIUrl":"10.3233/XST-230194","url":null,"abstract":"<p><strong>Background: </strong>In many developing countries, a significant number of breast cancer patients are unable to receive timely treatment due to a large population base, high patient numbers, and limited medical resources.</p><p><strong>Objective: </strong>This paper proposes a breast cancer assisted diagnosis system based on electronic medical records. The goal of this system is to address the limitations of existing systems, which primarily rely on structured electronic records and may miss crucial information stored in unstructured records.</p><p><strong>Methods: </strong>The proposed approach is a breast cancer assisted diagnosis system based on electronic medical records. The system utilizes breast cancer enhanced convolutional neural networks with semantic initialization filters (BC-INIT-CNN). It extracts highly relevant tumor markers from unstructured medical records to aid in breast cancer staging diagnosis and effectively utilizes the important information present in unstructured records.</p><p><strong>Results: </strong>The model's performance is assessed using various evaluation metrics. Such as accuracy, ROC curves, and Precision-Recall curves. Comparative analysis demonstrates that the BC-INIT-CNN model outperforms several existing methods in terms of accuracy and computational efficiency.</p><p><strong>Conclusions: </strong>The proposed breast cancer assisted diagnosis system based on BC-INIT-CNN showcases the potential to address the challenges faced by developing countries in providing timely treatment to breast cancer patients. By leveraging unstructured medical records and extracting relevant tumor markers, the system enables accurate staging diagnosis and enhances the utilization of valuable information.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"395-413"},"PeriodicalIF":3.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139378672","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":"Auto-evaluation of skull radiograph accuracy using unsupervised anomaly detection.","authors":"Haruyuki Watanabe, Yuina Ezawa, Eri Matsuyama, Yohan Kondo, Norio Hayashi, Sho Maruyama, Toshihiro Ogura, Masayuki Shimosegawa","doi":"10.3233/XST-230431","DOIUrl":"10.3233/XST-230431","url":null,"abstract":"<p><strong>Background: </strong>Radiography plays an important role in medical care, and accurate positioning is essential for providing optimal quality images. Radiographs with insufficient diagnostic value are rejected, and retakes are required. However, determining the suitability of retaking radiographs is a qualitative evaluation.</p><p><strong>Objective: </strong>To evaluate skull radiograph accuracy automatically using an unsupervised learning-based autoencoder (AE) and a variational autoencoder (VAE). In this study, we eliminated visual qualitative evaluation and used unsupervised learning to identify skull radiography retakes from the quantitative evaluation.</p><p><strong>Methods: </strong>Five skull phantoms were imaged on radiographs, and 1,680 images were acquired. These images correspond to two categories: normal images captured at appropriate positions and images captured at inappropriate positions. This study verified the discriminatory ability of skull radiographs using anomaly detection methods.</p><p><strong>Results: </strong>The areas under the curves for AE and VAE were 0.7060 and 0.6707, respectively, in receiver operating characteristic analysis. Our proposed method showed a higher discrimination ability than those of previous studies which had an accuracy of 52%.</p><p><strong>Conclusions: </strong>Our findings suggest that the proposed method has high classification accuracy in determining the suitability of retaking skull radiographs. Automation of optimal image consideration, whether or not to retake radiographs, contributes to improving operational efficiency in busy X-ray imaging operations.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"1151-1162"},"PeriodicalIF":1.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141472035","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":"Adaptive prior image constrained total generalized variation for low-dose dynamic cerebral perfusion CT reconstruction.","authors":"Shanzhou Niu, Shuo Li, Shuyan Huang, Lijing Liang, Sizhou Tang, Tinghua Wang, Gaohang Yu, Tianye Niu, Jing Wang, Jianhua Ma","doi":"10.3233/XST-240104","DOIUrl":"10.3233/XST-240104","url":null,"abstract":"<p><strong>Background: </strong>Dynamic cerebral perfusion CT (DCPCT) can provide valuable insight into cerebral hemodynamics by visualizing changes in blood within the brain. However, the associated high radiation dose of the standard DCPCT scanning protocol has been a great concern for the patient and radiation physics. Minimizing the x-ray exposure to patients has been a major effort in the DCPCT examination. A simple and cost-effective approach to achieve low-dose DCPCT imaging is to lower the x-ray tube current in data acquisition. However, the image quality of low-dose DCPCT will be degraded because of the excessive quantum noise.</p><p><strong>Objective: </strong>To obtain high-quality DCPCT images, we present a statistical iterative reconstruction (SIR) algorithm based on penalized weighted least squares (PWLS) using adaptive prior image constrained total generalized variation (APICTGV) regularization (PWLS-APICTGV).</p><p><strong>Methods: </strong>APICTGV regularization uses the precontrast scanned high-quality CT image as an adaptive structural prior for low-dose PWLS reconstruction. Thus, the image quality of low-dose DCPCT is improved while essential features of targe image are well preserved. An alternating optimization algorithm is developed to solve the cost function of the PWLS-APICTGV reconstruction.</p><p><strong>Results: </strong>PWLS-APICTGV algorithm was evaluated using a digital brain perfusion phantom and patient data. Compared to other competing algorithms, the PWLS-APICTGV algorithm shows better noise reduction and structural details preservation. Furthermore, the PWLS-APICTGV algorithm can generate more accurate cerebral blood flow (CBF) map than that of other reconstruction methods.</p><p><strong>Conclusions: </strong>PWLS-APICTGV algorithm can significantly suppress noise while preserving the important features of the reconstructed DCPCT image, thus achieving a great improvement in low-dose DCPCT imaging.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"1429-1447"},"PeriodicalIF":1.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142299655","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":"Classification of benign and malignant pulmonary nodule based on local-global hybrid network.","authors":"Xin Zhang, Ping Yang, Ji Tian, Fan Wen, Xi Chen, Tayyab Muhammad","doi":"10.3233/XST-230291","DOIUrl":"10.3233/XST-230291","url":null,"abstract":"<p><strong>Background: </strong>The accurate classification of pulmonary nodules has great application value in assisting doctors in diagnosing conditions and meeting clinical needs. However, the complexity and heterogeneity of pulmonary nodules make it difficult to extract valuable characteristics of pulmonary nodules, so it is still challenging to achieve high-accuracy classification of pulmonary nodules.</p><p><strong>Objective: </strong>In this paper, we propose a local-global hybrid network (LGHNet) to jointly model local and global information to improve the classification ability of benign and malignant pulmonary nodules.</p><p><strong>Methods: </strong>First, we introduce the multi-scale local (MSL) block, which splits the input tensor into multiple channel groups, utilizing dilated convolutions with different dilation rates and efficient channel attention to extract fine-grained local information at different scales. Secondly, we design the hybrid attention (HA) block to capture long-range dependencies in spatial and channel dimensions to enhance the representation of global features.</p><p><strong>Results: </strong>Experiments are carried out on the publicly available LIDC-IDRI and LUNGx datasets, and the accuracy, sensitivity, precision, specificity, and area under the curve (AUC) of the LIDC-IDRI dataset are 94.42%, 94.25%, 93.05%, 92.87%, and 97.26%, respectively. The AUC on the LUNGx dataset was 79.26%.</p><p><strong>Conclusion: </strong>The above classification results are superior to the state-of-the-art methods, indicating that the network has better classification performance and generalization ability.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"689-706"},"PeriodicalIF":3.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139566189","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}