This study sets the objective to involve undergraduate students in the evaluation of radiologic sciences and medical imaging technology programmes in Sudanese universities. Based on the analysis of survey results in w...This study sets the objective to involve undergraduate students in the evaluation of radiologic sciences and medical imaging technology programmes in Sudanese universities. Based on the analysis of survey results in which the participants (BSc students undertaking radiologic sciences and medical imaging technology programmes at university level) are asked to answer both closed and open-ended questions, the study seeks to reveal the participants’ perceptions and introspections about the radiologic sciences and medical imaging technology programmes in Sudan. It also attempts to explore the participants’ suggestions and recommendations as to enhance the quality of these programmes with an eye to helping syllabus designers to improve these programmes, thereby bettering healthcare services for the larger good to the community. A brief cross-sectional survey is completed by a total of 105 radiologic sciences and medical imaging technology students, i.e. 39 (37.1%) third-year students and 66 (62.9%) fourth-year students. The majority of participants is satisfied with the programmes, indicating that they are up-to-date and ran abreast with the latest developments in the field. Very few suggest that the programmes should be reviewed for revision, implying that there is room for improvement. Some participants recommend that more training hours in modern imaging modalities (e.g. MRI, CT and U/S) should be introduced. Only one participant recommends the introduction of advanced training centres.展开更多
Brain tumor is a global issue due to which several people suffer,and its early diagnosis can help in the treatment in a more efficient manner.Identifying different types of brain tumors,including gliomas,meningiomas,p...Brain tumor is a global issue due to which several people suffer,and its early diagnosis can help in the treatment in a more efficient manner.Identifying different types of brain tumors,including gliomas,meningiomas,pituitary tumors,as well as confirming the absence of tumors,poses a significant challenge using MRI images.Current approaches predominantly rely on traditional machine learning and basic deep learning methods for image classification.These methods often rely on manual feature extraction and basic convolutional neural networks(CNNs).The limitations include inadequate accuracy,poor generalization of new data,and limited ability to manage the high variability in MRI images.Utilizing the EfficientNetB3 architecture,this study presents a groundbreaking approach in the computational engineering domain,enhancing MRI-based brain tumor classification.Our approach highlights a major advancement in employing sophisticated machine learning techniques within Computer Science and Engineering,showcasing a highly accurate framework with significant potential for healthcare technologies.The model achieves an outstanding 99%accuracy,exhibiting balanced precision,recall,and F1-scores across all tumor types,as detailed in the classification report.This successful implementation demonstrates the model’s potential as an essential tool for diagnosing and classifying brain tumors,marking a notable improvement over current methods.The integration of such advanced computational techniques in medical diagnostics can significantly enhance accuracy and efficiency,paving the way for wider application.This research highlights the revolutionary impact of deep learning technologies in improving diagnostic processes and patient outcomes in neuro-oncology.展开更多
In alignment with the 20th National Congress of the Communist Party of China’s commitment to establish a healthier nation,the focus on healthcare strategically prioritizes human well-being.This necessitates redefinin...In alignment with the 20th National Congress of the Communist Party of China’s commitment to establish a healthier nation,the focus on healthcare strategically prioritizes human well-being.This necessitates redefining medical service delivery.Consequently,the training of medical students and continuing education is Challenged.In recent years,the notion of incorporating“ideological and political education within the curriculum”is becoming more explicit,especially within university talent training.This paper,exploring the unique attributes of professional medical imaging student training and continuing education,proposes the innovative model of“3+4+2”ideological and political education at the First Affiliated Hospital of Dalian Medical University.It introduces a new system of“school-family-society”tripartite co-education,a new mechanism of“party committee-functional departments-teaching and research office-teachers”four-level linkage,and a novel approach of“party branch-teaching and research office”dual-wheel drive,integrating value shaping,knowledge imparting,and ability cultivation.The goal is to nurture Party talents and educate patriots,thereby enhancing the effectiveness of“comprehensive education”and bolstering the construction of new medical sciences.展开更多
Signal to noise ratio in ultrasound medical images captured through the digital camera is poorer,resulting in an inaccurate diagnosis.As a result,it needs an efficient despeckling method for ultrasound images in clinic...Signal to noise ratio in ultrasound medical images captured through the digital camera is poorer,resulting in an inaccurate diagnosis.As a result,it needs an efficient despeckling method for ultrasound images in clinical practice and tel-emedicine.This article proposes a novel adaptive fuzzyfilter based on the direc-tionality and translation invariant property of the Non-Sub sampled Contour-let Transform(NSCT).Since speckle-noise causes fuzziness in ultrasound images,fuzzy logic may be a straightforward technique to derive the output from the noisy images.Thisfiltering method comprises detection andfiltering stages.First,image regions classify at the detection stage by applying fuzzy inference to the directional difference obtained from the NSCT noisy image.Then,the system adaptively selects the better-suitedfilter for the specific image region,resulting in significant speckle noise suppression and retention of detailed features.The suggested approach uses a weighted averagefilter to distinguish between noise and edges at thefiltering stage.In addition,we apply a structural similarity mea-sure as a tuning parameter depending on the kind of noise in the ultrasound pic-tures.The proposed methodology shows that the proposed fuzzy adaptivefilter effectively suppresses speckle noise while preserving edges and image detailed structures compared to existing approaches.展开更多
在重庆医科大学附属第一医院金山院区进行的心脏磁共振(cardiovascular magnetic resonance,CMR)扫描技术短期进修中,医学影像技术专业的进修生遇到了几个主要问题:不熟悉扫描流程、扫描序列的功能及其参数设置,以及对常见心脏病诊断的...在重庆医科大学附属第一医院金山院区进行的心脏磁共振(cardiovascular magnetic resonance,CMR)扫描技术短期进修中,医学影像技术专业的进修生遇到了几个主要问题:不熟悉扫描流程、扫描序列的功能及其参数设置,以及对常见心脏病诊断的理解不全面,导致无法有效处理扫描过程中的参数冲突。此外,进修生迫切希望能在短时间内掌握扫描技术,以便回归工作岗位时应用所学知识。针对上述问题和需求,笔者提出了一套教学改进措施,包括针对扫描技术规范、扫描序列及其参数特性的详细讲解,解决扫描过程中的参数冲突的策略,以及常见心脏病诊断的关键点。教学中采用了理论与实践相结合的方法,使进修生能在短期内有效掌握CMR扫描技术。通过这种教学方法,进修生不仅在短暂的学习期间基本掌握了CMR扫描技术,回到各自单位后,也能够独立开展相关技术项目,显著提高了进修生的技术掌握度和满意度。展开更多
文摘This study sets the objective to involve undergraduate students in the evaluation of radiologic sciences and medical imaging technology programmes in Sudanese universities. Based on the analysis of survey results in which the participants (BSc students undertaking radiologic sciences and medical imaging technology programmes at university level) are asked to answer both closed and open-ended questions, the study seeks to reveal the participants’ perceptions and introspections about the radiologic sciences and medical imaging technology programmes in Sudan. It also attempts to explore the participants’ suggestions and recommendations as to enhance the quality of these programmes with an eye to helping syllabus designers to improve these programmes, thereby bettering healthcare services for the larger good to the community. A brief cross-sectional survey is completed by a total of 105 radiologic sciences and medical imaging technology students, i.e. 39 (37.1%) third-year students and 66 (62.9%) fourth-year students. The majority of participants is satisfied with the programmes, indicating that they are up-to-date and ran abreast with the latest developments in the field. Very few suggest that the programmes should be reviewed for revision, implying that there is room for improvement. Some participants recommend that more training hours in modern imaging modalities (e.g. MRI, CT and U/S) should be introduced. Only one participant recommends the introduction of advanced training centres.
基金supported by the Researchers Supporting Program at King Saud University.Researchers Supporting Project number(RSPD2024R867),King Saud University,Riyadh,Saudi Arabia.
文摘Brain tumor is a global issue due to which several people suffer,and its early diagnosis can help in the treatment in a more efficient manner.Identifying different types of brain tumors,including gliomas,meningiomas,pituitary tumors,as well as confirming the absence of tumors,poses a significant challenge using MRI images.Current approaches predominantly rely on traditional machine learning and basic deep learning methods for image classification.These methods often rely on manual feature extraction and basic convolutional neural networks(CNNs).The limitations include inadequate accuracy,poor generalization of new data,and limited ability to manage the high variability in MRI images.Utilizing the EfficientNetB3 architecture,this study presents a groundbreaking approach in the computational engineering domain,enhancing MRI-based brain tumor classification.Our approach highlights a major advancement in employing sophisticated machine learning techniques within Computer Science and Engineering,showcasing a highly accurate framework with significant potential for healthcare technologies.The model achieves an outstanding 99%accuracy,exhibiting balanced precision,recall,and F1-scores across all tumor types,as detailed in the classification report.This successful implementation demonstrates the model’s potential as an essential tool for diagnosing and classifying brain tumors,marking a notable improvement over current methods.The integration of such advanced computational techniques in medical diagnostics can significantly enhance accuracy and efficiency,paving the way for wider application.This research highlights the revolutionary impact of deep learning technologies in improving diagnostic processes and patient outcomes in neuro-oncology.
文摘In alignment with the 20th National Congress of the Communist Party of China’s commitment to establish a healthier nation,the focus on healthcare strategically prioritizes human well-being.This necessitates redefining medical service delivery.Consequently,the training of medical students and continuing education is Challenged.In recent years,the notion of incorporating“ideological and political education within the curriculum”is becoming more explicit,especially within university talent training.This paper,exploring the unique attributes of professional medical imaging student training and continuing education,proposes the innovative model of“3+4+2”ideological and political education at the First Affiliated Hospital of Dalian Medical University.It introduces a new system of“school-family-society”tripartite co-education,a new mechanism of“party committee-functional departments-teaching and research office-teachers”four-level linkage,and a novel approach of“party branch-teaching and research office”dual-wheel drive,integrating value shaping,knowledge imparting,and ability cultivation.The goal is to nurture Party talents and educate patriots,thereby enhancing the effectiveness of“comprehensive education”and bolstering the construction of new medical sciences.
文摘Signal to noise ratio in ultrasound medical images captured through the digital camera is poorer,resulting in an inaccurate diagnosis.As a result,it needs an efficient despeckling method for ultrasound images in clinical practice and tel-emedicine.This article proposes a novel adaptive fuzzyfilter based on the direc-tionality and translation invariant property of the Non-Sub sampled Contour-let Transform(NSCT).Since speckle-noise causes fuzziness in ultrasound images,fuzzy logic may be a straightforward technique to derive the output from the noisy images.Thisfiltering method comprises detection andfiltering stages.First,image regions classify at the detection stage by applying fuzzy inference to the directional difference obtained from the NSCT noisy image.Then,the system adaptively selects the better-suitedfilter for the specific image region,resulting in significant speckle noise suppression and retention of detailed features.The suggested approach uses a weighted averagefilter to distinguish between noise and edges at thefiltering stage.In addition,we apply a structural similarity mea-sure as a tuning parameter depending on the kind of noise in the ultrasound pic-tures.The proposed methodology shows that the proposed fuzzy adaptivefilter effectively suppresses speckle noise while preserving edges and image detailed structures compared to existing approaches.
文摘在重庆医科大学附属第一医院金山院区进行的心脏磁共振(cardiovascular magnetic resonance,CMR)扫描技术短期进修中,医学影像技术专业的进修生遇到了几个主要问题:不熟悉扫描流程、扫描序列的功能及其参数设置,以及对常见心脏病诊断的理解不全面,导致无法有效处理扫描过程中的参数冲突。此外,进修生迫切希望能在短时间内掌握扫描技术,以便回归工作岗位时应用所学知识。针对上述问题和需求,笔者提出了一套教学改进措施,包括针对扫描技术规范、扫描序列及其参数特性的详细讲解,解决扫描过程中的参数冲突的策略,以及常见心脏病诊断的关键点。教学中采用了理论与实践相结合的方法,使进修生能在短期内有效掌握CMR扫描技术。通过这种教学方法,进修生不仅在短暂的学习期间基本掌握了CMR扫描技术,回到各自单位后,也能够独立开展相关技术项目,显著提高了进修生的技术掌握度和满意度。
