Journal of X-Ray Science and Technology This is an RSS file. You can use it to subscribe to this data in your favourite RSS reader or to display this data on your own website or blog.

Projection domain decomposition denoising algorithm based on low rank and similarity-based regularization
CONCLUSIONS: The proposed algorithm has a significantly improved noise reduction compared to other competing approach in dual-energy CT. Meanwhile, the LRSBR method exhibits outstanding performance in preserving edges and fine structures, making it practical for PDD applications.PMID:38640141 | DOI:10.3233/XST-230248 (Source: Journal of X-Ray Science and Technology)
Source: Journal of X-Ray Science and Technology - April 19, 2024 Category: Radiology Authors: Chang Lu Zhenye Han Jing Zou Source Type: research

A user-friendly deep learning application for accurate lung cancer diagnosis
CONCLUSIONS: The model provided means for storing and updating patients' data directly on the interface which allowed the results to be readily available for the health care providers. The developed system will improve clinical communication and information exchange. Moreover, it can manage efforts by generating correlated and coherent summaries of cancer diagnoses.PMID:38607727 | DOI:10.3233/XST-230255 (Source: Journal of X-Ray Science and Technology)
Source: Journal of X-Ray Science and Technology - April 12, 2024 Category: Radiology Authors: Duong Thanh Tai Nguyen Tan Nhu Pham Anh Tuan Abdelmoneim Suleiman Hiba Omer Zahra Alirezaei David Bradley James C L Chow Source Type: research

DDA-SSNets: Dual decoder attention-based semantic segmentation networks for COVID-19 infection segmentation and classification using chest X-Ray images
CONCLUSIONS: The results show that the proposed DDA-SegNet has superior performance in the segmentation of lung lobes and COVID-19-infected regions in CXRs, along with improved severity grading compared to the DDA-UNet and improved accuracy of the GADCNet classifier in classifying the CXRs into COVID-19, and non-COVID-19.PMID:38607728 | DOI:10.3233/XST-230421 (Source: Journal of X-Ray Science and Technology)
Source: Journal of X-Ray Science and Technology - April 12, 2024 Category: Radiology Authors: Anandbabu Gopatoti Ramya Jayakumar Poornaiah Billa Vijayalakshmi Patteeswaran Source Type: research

Predicting patient-specific organ doses from thoracic CT examinations using support vector regression algorithm
CONCLUSIONS: By combined utilization of the SVR algorithm and thoracic radiomics features, patient-specific thoracic organ doses could be predicted accurately, fast, and robustly in one second even using one single CPU core.PMID:38607729 | DOI:10.3233/XST-240015 (Source: Journal of X-Ray Science and Technology)
Source: Journal of X-Ray Science and Technology - April 12, 2024 Category: Radiology Authors: Wencheng Shao Xin Lin Ying Huang Liangyong Qu Zhuo Weihai Haikuan Liu Source Type: research

A user-friendly deep learning application for accurate lung cancer diagnosis
CONCLUSIONS: The model provided means for storing and updating patients' data directly on the interface which allowed the results to be readily available for the health care providers. The developed system will improve clinical communication and information exchange. Moreover, it can manage efforts by generating correlated and coherent summaries of cancer diagnoses.PMID:38607727 | DOI:10.3233/XST-230255 (Source: Journal of X-Ray Science and Technology)
Source: Journal of X-Ray Science and Technology - April 12, 2024 Category: Radiology Authors: Duong Thanh Tai Nguyen Tan Nhu Pham Anh Tuan Abdelmoneim Suleiman Hiba Omer Zahra Alirezaei David Bradley James C L Chow Source Type: research

DDA-SSNets: Dual decoder attention-based semantic segmentation networks for COVID-19 infection segmentation and classification using chest X-Ray images
CONCLUSIONS: The results show that the proposed DDA-SegNet has superior performance in the segmentation of lung lobes and COVID-19-infected regions in CXRs, along with improved severity grading compared to the DDA-UNet and improved accuracy of the GADCNet classifier in classifying the CXRs into COVID-19, and non-COVID-19.PMID:38607728 | DOI:10.3233/XST-230421 (Source: Journal of X-Ray Science and Technology)
Source: Journal of X-Ray Science and Technology - April 12, 2024 Category: Radiology Authors: Anandbabu Gopatoti Ramya Jayakumar Poornaiah Billa Vijayalakshmi Patteeswaran Source Type: research

Predicting patient-specific organ doses from thoracic CT examinations using support vector regression algorithm
CONCLUSIONS: By combined utilization of the SVR algorithm and thoracic radiomics features, patient-specific thoracic organ doses could be predicted accurately, fast, and robustly in one second even using one single CPU core.PMID:38607729 | DOI:10.3233/XST-240015 (Source: Journal of X-Ray Science and Technology)
Source: Journal of X-Ray Science and Technology - April 12, 2024 Category: Radiology Authors: Wencheng Shao Xin Lin Ying Huang Liangyong Qu Zhuo Weihai Haikuan Liu Source Type: research

A user-friendly deep learning application for accurate lung cancer diagnosis
CONCLUSIONS: The model provided means for storing and updating patients' data directly on the interface which allowed the results to be readily available for the health care providers. The developed system will improve clinical communication and information exchange. Moreover, it can manage efforts by generating correlated and coherent summaries of cancer diagnoses.PMID:38607727 | DOI:10.3233/XST-230255 (Source: Journal of X-Ray Science and Technology)
Source: Journal of X-Ray Science and Technology - April 12, 2024 Category: Radiology Authors: Duong Thanh Tai Nguyen Tan Nhu Pham Anh Tuan Abdelmoneim Suleiman Hiba Omer Zahra Alirezaei David Bradley James C L Chow Source Type: research

DDA-SSNets: Dual decoder attention-based semantic segmentation networks for COVID-19 infection segmentation and classification using chest X-Ray images
CONCLUSIONS: The results show that the proposed DDA-SegNet has superior performance in the segmentation of lung lobes and COVID-19-infected regions in CXRs, along with improved severity grading compared to the DDA-UNet and improved accuracy of the GADCNet classifier in classifying the CXRs into COVID-19, and non-COVID-19.PMID:38607728 | DOI:10.3233/XST-230421 (Source: Journal of X-Ray Science and Technology)
Source: Journal of X-Ray Science and Technology - April 12, 2024 Category: Radiology Authors: Anandbabu Gopatoti Ramya Jayakumar Poornaiah Billa Vijayalakshmi Patteeswaran Source Type: research

Predicting patient-specific organ doses from thoracic CT examinations using support vector regression algorithm
CONCLUSIONS: By combined utilization of the SVR algorithm and thoracic radiomics features, patient-specific thoracic organ doses could be predicted accurately, fast, and robustly in one second even using one single CPU core.PMID:38607729 | DOI:10.3233/XST-240015 (Source: Journal of X-Ray Science and Technology)
Source: Journal of X-Ray Science and Technology - April 12, 2024 Category: Radiology Authors: Wencheng Shao Xin Lin Ying Huang Liangyong Qu Zhuo Weihai Haikuan Liu Source Type: research

A user-friendly deep learning application for accurate lung cancer diagnosis
CONCLUSIONS: The model provided means for storing and updating patients' data directly on the interface which allowed the results to be readily available for the health care providers. The developed system will improve clinical communication and information exchange. Moreover, it can manage efforts by generating correlated and coherent summaries of cancer diagnoses.PMID:38607727 | DOI:10.3233/XST-230255 (Source: Journal of X-Ray Science and Technology)
Source: Journal of X-Ray Science and Technology - April 12, 2024 Category: Radiology Authors: Duong Thanh Tai Nguyen Tan Nhu Pham Anh Tuan Abdelmoneim Suleiman Hiba Omer Zahra Alirezaei David Bradley James C L Chow Source Type: research

DDA-SSNets: Dual decoder attention-based semantic segmentation networks for COVID-19 infection segmentation and classification using chest X-Ray images
CONCLUSIONS: The results show that the proposed DDA-SegNet has superior performance in the segmentation of lung lobes and COVID-19-infected regions in CXRs, along with improved severity grading compared to the DDA-UNet and improved accuracy of the GADCNet classifier in classifying the CXRs into COVID-19, and non-COVID-19.PMID:38607728 | DOI:10.3233/XST-230421 (Source: Journal of X-Ray Science and Technology)
Source: Journal of X-Ray Science and Technology - April 12, 2024 Category: Radiology Authors: Anandbabu Gopatoti Ramya Jayakumar Poornaiah Billa Vijayalakshmi Patteeswaran Source Type: research

Predicting patient-specific organ doses from thoracic CT examinations using support vector regression algorithm
CONCLUSIONS: By combined utilization of the SVR algorithm and thoracic radiomics features, patient-specific thoracic organ doses could be predicted accurately, fast, and robustly in one second even using one single CPU core.PMID:38607729 | DOI:10.3233/XST-240015 (Source: Journal of X-Ray Science and Technology)
Source: Journal of X-Ray Science and Technology - April 12, 2024 Category: Radiology Authors: Wencheng Shao Xin Lin Ying Huang Liangyong Qu Zhuo Weihai Haikuan Liu Source Type: research

A user-friendly deep learning application for accurate lung cancer diagnosis
CONCLUSIONS: The model provided means for storing and updating patients' data directly on the interface which allowed the results to be readily available for the health care providers. The developed system will improve clinical communication and information exchange. Moreover, it can manage efforts by generating correlated and coherent summaries of cancer diagnoses.PMID:38607727 | DOI:10.3233/XST-230255 (Source: Journal of X-Ray Science and Technology)
Source: Journal of X-Ray Science and Technology - April 12, 2024 Category: Radiology Authors: Duong Thanh Tai Nguyen Tan Nhu Pham Anh Tuan Abdelmoneim Suleiman Hiba Omer Zahra Alirezaei David Bradley James C L Chow Source Type: research

DDA-SSNets: Dual decoder attention-based semantic segmentation networks for COVID-19 infection segmentation and classification using chest X-Ray images
CONCLUSIONS: The results show that the proposed DDA-SegNet has superior performance in the segmentation of lung lobes and COVID-19-infected regions in CXRs, along with improved severity grading compared to the DDA-UNet and improved accuracy of the GADCNet classifier in classifying the CXRs into COVID-19, and non-COVID-19.PMID:38607728 | DOI:10.3233/XST-230421 (Source: Journal of X-Ray Science and Technology)
Source: Journal of X-Ray Science and Technology - April 12, 2024 Category: Radiology Authors: Anandbabu Gopatoti Ramya Jayakumar Poornaiah Billa Vijayalakshmi Patteeswaran Source Type: research