INTRODUCTION: Accurate tumor segmentation is a prerequisite for reliable diagnosis and treatment of brain cancer. Gliomas, a highly prevalent and life-threatening type of brain tumor, pose a challenge for segmentation due to the intricate nature of brain structures and unpredictable appearances on brain MRI images.OBJECTIVES: Current methods for brain tumor segmentation mostly rely on deep convolutional neural networks, which suffer from significant loss of feature information during encoding and decoding and the inability to capture tumor contours in detail.METHODS: To address these challenges, this study rethinks the network architecture for MRI brain tumor segmentation. It proposes ARM-Net: an improved method for MRI brain tumor segmentation based on attention mechanisms and residual modules. Firstly, inverted external attention and dilated gated attention are employed in the last two layers of the encoder to enable the network to interact with both lesion areas and global information, facilitating better interaction among the four modalities. Secondly, different numbers of Res-Paths are added in the encoder's first two layers and the decoder's last two layers to effectively mitigate the semantic gap issues caused by traditional skip connections.RESULTS: Experiments on the BraTS 2019 dataset demonstrate that ARM-Net outperforms other similar models in terms of segmentation performance.CONCLUSION: The experiment showed that the ARM-Net model could segment the contour structure of the tumor better than other methods.
{"title":"ARM-Net: Improved MRI brain tumor segmentation method based on attentional mechanism and residual module","authors":"MingHu","doi":"10.4108/eetel.5953","DOIUrl":"https://doi.org/10.4108/eetel.5953","url":null,"abstract":"INTRODUCTION: Accurate tumor segmentation is a prerequisite for reliable diagnosis and treatment of brain cancer. Gliomas, a highly prevalent and life-threatening type of brain tumor, pose a challenge for segmentation due to the intricate nature of brain structures and unpredictable appearances on brain MRI images.OBJECTIVES: Current methods for brain tumor segmentation mostly rely on deep convolutional neural networks, which suffer from significant loss of feature information during encoding and decoding and the inability to capture tumor contours in detail.METHODS: To address these challenges, this study rethinks the network architecture for MRI brain tumor segmentation. It proposes ARM-Net: an improved method for MRI brain tumor segmentation based on attention mechanisms and residual modules. Firstly, inverted external attention and dilated gated attention are employed in the last two layers of the encoder to enable the network to interact with both lesion areas and global information, facilitating better interaction among the four modalities. Secondly, different numbers of Res-Paths are added in the encoder's first two layers and the decoder's last two layers to effectively mitigate the semantic gap issues caused by traditional skip connections.RESULTS: Experiments on the BraTS 2019 dataset demonstrate that ARM-Net outperforms other similar models in terms of segmentation performance.CONCLUSION: The experiment showed that the ARM-Net model could segment the contour structure of the tumor better than other methods. ","PeriodicalId":502644,"journal":{"name":"EAI Endorsed Transactions on e-Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141799785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Image segmentation is an important research direction in medical image processing tasks, and it is also a challenging task in the field of computer vision. At present, there have been many image segmentation methods, including traditional segmentation methods and deep learning-based segmentation methods. Through the understanding and learning of the current situation in the field of medical image segmentation, this paper systematically combs it. Firstly, it briefly introduces the traditional image segmentation methods such as threshold method, region method and graph cut method, and focuses on the commonly used network architectures based on deep learning such as CNN, FCN, U-Net, SegNet, PSPNet, Mask R-CNN. At the same time, the application in medical image segmentation is expounded. Finally, the challenges and development opportunities of medical image segmentation technology based on deep learning are discussed.
{"title":"Applications of Image Segmentation Techniques in Medical Images","authors":"Yang-yang Hou","doi":"10.4108/eetel.4449","DOIUrl":"https://doi.org/10.4108/eetel.4449","url":null,"abstract":"Image segmentation is an important research direction in medical image processing tasks, and it is also a challenging task in the field of computer vision. At present, there have been many image segmentation methods, including traditional segmentation methods and deep learning-based segmentation methods. Through the understanding and learning of the current situation in the field of medical image segmentation, this paper systematically combs it. Firstly, it briefly introduces the traditional image segmentation methods such as threshold method, region method and graph cut method, and focuses on the commonly used network architectures based on deep learning such as CNN, FCN, U-Net, SegNet, PSPNet, Mask R-CNN. At the same time, the application in medical image segmentation is expounded. Finally, the challenges and development opportunities of medical image segmentation technology based on deep learning are discussed.","PeriodicalId":502644,"journal":{"name":"EAI Endorsed Transactions on e-Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141820668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Liver cancer is a disease with a high incidence and high probability of deterioration, and for the rapid diagnosis of liver disease, CT scans must be used to segment the liver tumors. For the past few years, with the rapid development of deep learning, many deep learning methods for liver tumor segmentation using abdominal computed tomography (CT) images have appeared, and the clinical application of these methods is of important significance for computer-aided diagnosis of liver tumors. The U-Net, with its unique U-shape network structure, exhibits excellent performance in medical image segmentation field and has been extensively utilized in various medical image segmentation applications. In this paper, we summarize the researches of U-Net and its improved networks in CT image segmentation of liver tumors by deep learning methods and classify various U-Net-based convolutional neural networks (CNNs) into 2D (two-dimensional), 3D (three-dimensional), and 2.5D (2.5-dimensional). In this paper, 2D, 3D, and 2.5D convolutional neural networks are summarized. In addition, this paper summarizes the advantages and disadvantages as well as the improvement methods of each type of network, which provides a useful reference for the studies of deep learning based on liver tumor segmentation field. Finally, this paper envisions future research trends for deep learning segmentation methods in the context of liver tumors.
{"title":"Liver tumor segmentation method based on U-Net architecture: a review","authors":"Biao Wang, Chunfeng Yang","doi":"10.4108/eetel.5263","DOIUrl":"https://doi.org/10.4108/eetel.5263","url":null,"abstract":"Liver cancer is a disease with a high incidence and high probability of deterioration, and for the rapid diagnosis of liver disease, CT scans must be used to segment the liver tumors. For the past few years, with the rapid development of deep learning, many deep learning methods for liver tumor segmentation using abdominal computed tomography (CT) images have appeared, and the clinical application of these methods is of important significance for computer-aided diagnosis of liver tumors. The U-Net, with its unique U-shape network structure, exhibits excellent performance in medical image segmentation field and has been extensively utilized in various medical image segmentation applications. In this paper, we summarize the researches of U-Net and its improved networks in CT image segmentation of liver tumors by deep learning methods and classify various U-Net-based convolutional neural networks (CNNs) into 2D (two-dimensional), 3D (three-dimensional), and 2.5D (2.5-dimensional). In this paper, 2D, 3D, and 2.5D convolutional neural networks are summarized. In addition, this paper summarizes the advantages and disadvantages as well as the improvement methods of each type of network, which provides a useful reference for the studies of deep learning based on liver tumor segmentation field. Finally, this paper envisions future research trends for deep learning segmentation methods in the context of liver tumors.","PeriodicalId":502644,"journal":{"name":"EAI Endorsed Transactions on e-Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140231436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gesture recognition is an important and inevitable technology in modern times, its appearance and improvement greatly improve the convenience of people's lives, but also enrich people's lives. It has a wide range of applications in various fields. In daily life, it can carry out human-computer interaction and the use of smart home. In terms of medical treatment, it can help patients to recover and assist doctors to carry out experiments. In terms of entertainment, it allows users to interact with the game in an immersive manner. This paper chooses three technologies that deep learning plays a more prominent role in gesture recognition, namely CNNs, LSTM and transfer learning based on deep learning. They each have their own advantages and disadvantages. Because of the different principles of use, different techniques have different roles, such as CNNs can carry out feature extraction, LSTM can deal with long time series, transfer learning can transfer what is learned from another task to this task. Select different practical technologies according to different application scenarios, and make improvements in real time in practical applications. Gesture recognition based on deep learning has the advantages of good accuracy, robustness and real-time implementation, but it also bears the disadvantages of huge economic and time costs and high hardware requirements. Despite some challenges, researchers continue to optimize and improve the technology, and believe that in the future, gesture recognition technology will be more mature and valuable.
{"title":"Gesture Recognition Based on Deep Learning: A Review","authors":"Meng Wu","doi":"10.4108/eetel.5191","DOIUrl":"https://doi.org/10.4108/eetel.5191","url":null,"abstract":"Gesture recognition is an important and inevitable technology in modern times, its appearance and improvement greatly improve the convenience of people's lives, but also enrich people's lives. It has a wide range of applications in various fields. In daily life, it can carry out human-computer interaction and the use of smart home. In terms of medical treatment, it can help patients to recover and assist doctors to carry out experiments. In terms of entertainment, it allows users to interact with the game in an immersive manner. This paper chooses three technologies that deep learning plays a more prominent role in gesture recognition, namely CNNs, LSTM and transfer learning based on deep learning. They each have their own advantages and disadvantages. Because of the different principles of use, different techniques have different roles, such as CNNs can carry out feature extraction, LSTM can deal with long time series, transfer learning can transfer what is learned from another task to this task. Select different practical technologies according to different application scenarios, and make improvements in real time in practical applications. Gesture recognition based on deep learning has the advantages of good accuracy, robustness and real-time implementation, but it also bears the disadvantages of huge economic and time costs and high hardware requirements. Despite some challenges, researchers continue to optimize and improve the technology, and believe that in the future, gesture recognition technology will be more mature and valuable.","PeriodicalId":502644,"journal":{"name":"EAI Endorsed Transactions on e-Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140258818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Panagiota Pouliou, Despoina Ourda, V. Barkoukis, G. Palamas
In recent times, doping's prevalence in sports has gained substantial recognition, sparking a concerted effort from researchers, policymakers, and sports bodies to underscore the critical role of impactful anti-doping education initiatives. An exhaustive examination of current literature underscores a critical requirement for advanced educational interventions that can effectively combat the multifaceted challenges presented by doping across the spectrum of competitive and recreational athletes. In response to this exigency, this paper introduces an innovative paradigm to redefine anti-doping education through the fusion of virtual reality (VR) technology. This proposed approach seeks to leverage VR's immersive potential, offering dynamic and interactive learning experiences that authentically mirror the complexities surrounding doping decisions. By immersing athletes within lifelike scenarios, VR education aims to provide a nuanced understanding of the psychological and emotional facets associated with doping, all within a secure and controlled environment. However, while the potential of VR in anti-doping education is promising, it also necessitates addressing technical, ethical, and usability considerations, an aspect that this paper further explores.
{"title":"Empowering Young Athletes: Elevating Anti-Doping Education with Virtual Reality","authors":"Panagiota Pouliou, Despoina Ourda, V. Barkoukis, G. Palamas","doi":"10.4108/eetel.4537","DOIUrl":"https://doi.org/10.4108/eetel.4537","url":null,"abstract":"In recent times, doping's prevalence in sports has gained substantial recognition, sparking a concerted effort from researchers, policymakers, and sports bodies to underscore the critical role of impactful anti-doping education initiatives. An exhaustive examination of current literature underscores a critical requirement for advanced educational interventions that can effectively combat the multifaceted challenges presented by doping across the spectrum of competitive and recreational athletes. In response to this exigency, this paper introduces an innovative paradigm to redefine anti-doping education through the fusion of virtual reality (VR) technology. This proposed approach seeks to leverage VR's immersive potential, offering dynamic and interactive learning experiences that authentically mirror the complexities surrounding doping decisions. By immersing athletes within lifelike scenarios, VR education aims to provide a nuanced understanding of the psychological and emotional facets associated with doping, all within a secure and controlled environment. However, while the potential of VR in anti-doping education is promising, it also necessitates addressing technical, ethical, and usability considerations, an aspect that this paper further explores.","PeriodicalId":502644,"journal":{"name":"EAI Endorsed Transactions on e-Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139526552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accurate and robust segmentation of organs or lesions from medical images plays a vital role in many clinical applications such as diagnosis and treatment planning. With the massive increase in labeled data, deep learning has achieved great success in image segmentation. However, for medical images, the acquisition of labeled data is usually expensive because generating accurate annotations requires expertise and time, especially in 3D images. To reduce the cost of labeling, many approaches have been proposed in recent years to develop a high-performance medical image segmentation model to reduce the labeling data. For example, combining user interaction with deep neural networks to interactively perform image segmentation can reduce the labeling effort. Self-supervised learning methods utilize unlabeled data to train the model in a supervised manner, learn the basics and then perform knowledge transfer. Semi-supervised learning frameworks learn directly from a limited amount of labeled data and a large amount of unlabeled data to get high quality segmentation results. Weakly supervised learning approaches learn image segmentation from borders, graffiti, or image-level labels instead of using pixel-level labeling, which reduces the burden of labeling. However, the performance of weakly supervised learning and self-supervised learning is still limited on medical image segmentation tasks, especially on 3D medical images. In addition to this, a small amount of labeled data and a large amount of unlabeled data are more in line with actual clinical scenarios. Therefore, semi-supervised learning strategies become very important in the field of medical image processing.
{"title":"A review of research and development of semi-supervised learning strategies for medical image processing","authors":"Shengke Yang","doi":"10.4108/eetel.4822","DOIUrl":"https://doi.org/10.4108/eetel.4822","url":null,"abstract":"Accurate and robust segmentation of organs or lesions from medical images plays a vital role in many clinical applications such as diagnosis and treatment planning. With the massive increase in labeled data, deep learning has achieved great success in image segmentation. However, for medical images, the acquisition of labeled data is usually expensive because generating accurate annotations requires expertise and time, especially in 3D images. To reduce the cost of labeling, many approaches have been proposed in recent years to develop a high-performance medical image segmentation model to reduce the labeling data. For example, combining user interaction with deep neural networks to interactively perform image segmentation can reduce the labeling effort. Self-supervised learning methods utilize unlabeled data to train the model in a supervised manner, learn the basics and then perform knowledge transfer. Semi-supervised learning frameworks learn directly from a limited amount of labeled data and a large amount of unlabeled data to get high quality segmentation results. Weakly supervised learning approaches learn image segmentation from borders, graffiti, or image-level labels instead of using pixel-level labeling, which reduces the burden of labeling. However, the performance of weakly supervised learning and self-supervised learning is still limited on medical image segmentation tasks, especially on 3D medical images. In addition to this, a small amount of labeled data and a large amount of unlabeled data are more in line with actual clinical scenarios. Therefore, semi-supervised learning strategies become very important in the field of medical image processing.","PeriodicalId":502644,"journal":{"name":"EAI Endorsed Transactions on e-Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139528664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alzheimer's disease is one of the most common diseases in older adults, and as the disease progresses, the need for daily care increases. Caregivers of Alzheimer's Disease patients face a variety of stresses and work pressures. Receiving professional and continuous training is one of the effective ways to improve their skills and competencies. A new approach to education is microlearning, where microeducational content is provided to learners. Microlearning as a pedagogical technique focuses on designing learning modules through micro-steps in a digital media environment. These activities can be integrated into learners' daily lives and tasks. Unlike "traditional" e-learning methods, microlearning often favours technology delivered through push media, thus reducing the cognitive load on the learner. Microlearning educational methods have been shown to be effective and efficient in educating and delivering materials to caregivers of older adults with Alzheimer's disease. This paper begins with a brief introduction to microlearning. And it details the key features and benefits of microlearning. Microlearning offers potential benefits to Alzheimer's Disease patients and their caregivers with its concise and focused approach. Secondly, machine learning enhances the design and delivery of microlearning, helping to provide a more personalised and effective learning experience. Machine learning plays a role in the design of microlearning. To conclude, microlearning offers a promising avenue of support and care for Alzheimer's Disease patients. Microlearning also provides a valuable resource for carers and healthcare professionals to gain the knowledge and skills needed to provide effective care.
{"title":"Microlearning helps Alzheimer’s Disease Patients","authors":"Jiayao Hu","doi":"10.4108/eetel.4321","DOIUrl":"https://doi.org/10.4108/eetel.4321","url":null,"abstract":"Alzheimer's disease is one of the most common diseases in older adults, and as the disease progresses, the need for daily care increases. Caregivers of Alzheimer's Disease patients face a variety of stresses and work pressures. Receiving professional and continuous training is one of the effective ways to improve their skills and competencies. A new approach to education is microlearning, where microeducational content is provided to learners. Microlearning as a pedagogical technique focuses on designing learning modules through micro-steps in a digital media environment. These activities can be integrated into learners' daily lives and tasks. Unlike \"traditional\" e-learning methods, microlearning often favours technology delivered through push media, thus reducing the cognitive load on the learner. Microlearning educational methods have been shown to be effective and efficient in educating and delivering materials to caregivers of older adults with Alzheimer's disease. This paper begins with a brief introduction to microlearning. And it details the key features and benefits of microlearning. Microlearning offers potential benefits to Alzheimer's Disease patients and their caregivers with its concise and focused approach. Secondly, machine learning enhances the design and delivery of microlearning, helping to provide a more personalised and effective learning experience. Machine learning plays a role in the design of microlearning. To conclude, microlearning offers a promising avenue of support and care for Alzheimer's Disease patients. Microlearning also provides a valuable resource for carers and healthcare professionals to gain the knowledge and skills needed to provide effective care.","PeriodicalId":502644,"journal":{"name":"EAI Endorsed Transactions on e-Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139231387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article focuses on using MOOCs to learn image denoising techniques. It begins with an introduction to the concept of MOOCs - these innovative online learning platforms that offer a wide range of courses across disciplines, providing convenient and affordable learning opportunities for a global audience. It then explains the characteristics of MOOC's wide coverage, high flexibility, and different from traditional education models. It then introduces the advantages of MOOCs: accessibility and inclusiveness (open to anyone with an Internet connection), cost-effectiveness (a cost-effective alternative, many courses available for free), flexibility and self-paced learning (the ability to learn at your own pace), a diverse curriculum and global expertise. Then the concept of image denoising is introduced - image denoising is a basic process of digital image processing, and the common denoising methods are described: filter method and the applicable range of various filters, the advantages and disadvantages of wavelet change, the advantages of deep learning method and the principle of non-local mean denoising technology. It then describes how MOOCs can help learn image denoising: integrating course content, getting expert guidance, hands-on exercises and projects, and community and peer communication. In addition, it introduces the challenges encountered by MOOCs: high dropout rate, quality and credibility of MOOCs, lack of interaction and humanization in traditional classrooms, accessibility. The relationship between E-learning and MOOC is also introduced – E-learning and MOOC play complementary roles in modern education. MOOC provide a structured, flexible, cost-effective environment and a transformative educational experience for learning about biological image denoising.
{"title":"Use MOOC to learn image denoising techniques","authors":"Ting Zhao","doi":"10.4108/eetel.4396","DOIUrl":"https://doi.org/10.4108/eetel.4396","url":null,"abstract":"This article focuses on using MOOCs to learn image denoising techniques. It begins with an introduction to the concept of MOOCs - these innovative online learning platforms that offer a wide range of courses across disciplines, providing convenient and affordable learning opportunities for a global audience. It then explains the characteristics of MOOC's wide coverage, high flexibility, and different from traditional education models. It then introduces the advantages of MOOCs: accessibility and inclusiveness (open to anyone with an Internet connection), cost-effectiveness (a cost-effective alternative, many courses available for free), flexibility and self-paced learning (the ability to learn at your own pace), a diverse curriculum and global expertise. Then the concept of image denoising is introduced - image denoising is a basic process of digital image processing, and the common denoising methods are described: filter method and the applicable range of various filters, the advantages and disadvantages of wavelet change, the advantages of deep learning method and the principle of non-local mean denoising technology. It then describes how MOOCs can help learn image denoising: integrating course content, getting expert guidance, hands-on exercises and projects, and community and peer communication. In addition, it introduces the challenges encountered by MOOCs: high dropout rate, quality and credibility of MOOCs, lack of interaction and humanization in traditional classrooms, accessibility. The relationship between E-learning and MOOC is also introduced – E-learning and MOOC play complementary roles in modern education. MOOC provide a structured, flexible, cost-effective environment and a transformative educational experience for learning about biological image denoising.","PeriodicalId":502644,"journal":{"name":"EAI Endorsed Transactions on e-Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139253365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the increase of the aging population, the incidence rate of Alzheimer's disease (AD) is also rising. Faced with this challenge, e-learning, as an innovative educational method, has shown great potential in the care and management of Alzheimer's disease patients. This article reviews the application progress of E-learning in Alzheimer's disease. E-learning has revolutionized the field of education, providing learners with accessible and flexible learning opportunities. This paper provides an overview of various aspects of e-learning, including virtual classrooms, mobile learning, blended learning, Massive Open Online Courses (MOOCs), webinars, and the challenges associated with implementing e-learning.The background section explores the evolution of e-learning, highlighting its rise in popularity and the advancements in technology that have facilitated its growth. Virtual classrooms for adult learners are discussed, showcasing how these online platforms facilitate interactive and collaborative learning experiences. Mobile learning for adult learners is examined, emphasizing the convenience and accessibility provided by mobile devices in delivering educational content.Blended learning is another approach explored in this paper, which combines traditional face-to-face instruction with online components, offering a balanced learning experience. The benefits and challenges of implementing MOOCs, which provide free and open access to educational resources from top institutions, are also examined. Additionally, webinars are discussed as a popular method for delivering live online presentations and workshops to adult learners.Finally, the paper addresses the challenges of E-learning, including technological barriers, lack of personal interaction, and the need for self-discipline and motivation. Strategies for overcoming these challenges are suggested, such as providing technical support and fostering online community engagement.Overall, this paper provides valuable insights into the background and various approaches to E-learning, as well as the challenges encountered in its implementation. Understanding these aspects will help educators and institutions effectively harness the potential of E-learning to enhance adult education.
{"title":"E-learning for Alzheimer's Disease","authors":"Mengyao Zhao","doi":"10.4108/eetel.4258","DOIUrl":"https://doi.org/10.4108/eetel.4258","url":null,"abstract":"With the increase of the aging population, the incidence rate of Alzheimer's disease (AD) is also rising. Faced with this challenge, e-learning, as an innovative educational method, has shown great potential in the care and management of Alzheimer's disease patients. This article reviews the application progress of E-learning in Alzheimer's disease. E-learning has revolutionized the field of education, providing learners with accessible and flexible learning opportunities. This paper provides an overview of various aspects of e-learning, including virtual classrooms, mobile learning, blended learning, Massive Open Online Courses (MOOCs), webinars, and the challenges associated with implementing e-learning.The background section explores the evolution of e-learning, highlighting its rise in popularity and the advancements in technology that have facilitated its growth. Virtual classrooms for adult learners are discussed, showcasing how these online platforms facilitate interactive and collaborative learning experiences. Mobile learning for adult learners is examined, emphasizing the convenience and accessibility provided by mobile devices in delivering educational content.Blended learning is another approach explored in this paper, which combines traditional face-to-face instruction with online components, offering a balanced learning experience. The benefits and challenges of implementing MOOCs, which provide free and open access to educational resources from top institutions, are also examined. Additionally, webinars are discussed as a popular method for delivering live online presentations and workshops to adult learners.Finally, the paper addresses the challenges of E-learning, including technological barriers, lack of personal interaction, and the need for self-discipline and motivation. Strategies for overcoming these challenges are suggested, such as providing technical support and fostering online community engagement.Overall, this paper provides valuable insights into the background and various approaches to E-learning, as well as the challenges encountered in its implementation. Understanding these aspects will help educators and institutions effectively harness the potential of E-learning to enhance adult education.","PeriodicalId":502644,"journal":{"name":"EAI Endorsed Transactions on e-Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139271719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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