INTRODUCTION: When Diabetic Retinopathy (DR) is not identified promptly; it frequently results in sight impairment. To properly diagnose and treat DR, preprocessing of picture methods and precise prediction models are essential. With the help of numerous well-liked filters and a Deep CNN (Convolutional Neural Network) model, the comprehensive method for DR image preparation and prognosis presented in this research is described. Using the filters that focus boundaries and contours in the ocular pictures is the first step in the initial processing stage. This procedure tries to find anomalies linked to DR. By the usage of filters, the excellence of pictures can be developed and minimize disturbances, preserving critical information. The Deep CNN algorithm has been trained to generate forecasts on the cleaned retinal pictures following the phase of preprocessing. The filters efficiently eliminate interference without sacrificing vital data. Convolutional type layers, pooling type layers, and fully associated layers are used in the CNN framework, which was created especially for image categorization tasks, to acquire data and understand the relationships associated with DR. OBJECTIVES: Using image preprocessing techniques such as the Sobel, Wiener, Gaussian, and non-local mean filters is a promising approach for DR analysis. Then, predicting using a CNN completes the approach. These preprocessing filters enhance the images and prepare them for further examination. The pre-processed images are fed into a CNN model. The model extracts significant information from the images by identifying complex patterns. DR or classification may be predicted by the CNN model through training on a labeled dataset. METHODS: The Method Preprocessing is employed for enhancing the clarity and difference of retina fundus picture by removing noise and fluctuation. The preprocessing stage is utilized for the normalization of the pictures and non-uniform brightness adjustment in addition to contrast augmentation and noise mitigation to remove noises and improve the rate of precision of the subsequent processing stages. RESULTS: To improve image quality and reduce noise, preprocessing techniques including Sobel, Wiener, Gaussian, and non-local mean filters are frequently employed in image processing jobs. For a particular task, the non-local mean filter produces superior results; for enhanced performance, it may be advantageous to combine it with a CNN. Before supplying the processed images to the CNN for prediction, the non-local mean filter can assist reduce noise and improve image details. CONCLUSION: A promising method for DR analysis entails the use of image preprocessing methods such as the Sobel, Wiener, Gaussian, and non-local mean filters, followed by prediction using a CNN. These preprocessing filters improve the photos and get them ready for analysis. After being pre-processed, the photos are sent into a CNN model, which uses its capacity to discover intricate p
简介：糖尿病视网膜病变（DR）如不及时发现，往往会导致视力受损。要正确诊断和治疗糖尿病视网膜病变，图片预处理方法和精确预测模型至关重要。本研究中介绍的 DR 图像预处理和预测综合方法，借助了众多广受欢迎的滤波器和深度 CNN（卷积神经网络）模型。在初始处理阶段，第一步是使用滤镜聚焦眼部图像中的边界和轮廓。该步骤试图找出与 DR 有关的异常。通过使用滤波器，可以对图片进行精益求精的开发，最大限度地减少干扰，保留关键信息。深度 CNN 算法经过训练，可在预处理阶段之后对清洗后的视网膜图片生成预测。滤波器能有效消除干扰，同时不牺牲重要数据。卷积型层、池化型层和完全关联层被用于 CNN 框架，该框架是专门为图像分类任务而创建的，用于获取数据并理解与 DR 相关的关系。目标：使用索贝尔、维纳、高斯和非局部均值滤波器等图像预处理技术是一种很有前途的 DR 分析方法。然后，使用 CNN 进行预测就完成了这种方法。这些预处理滤波器可增强图像效果，为进一步检查做好准备。预处理后的图像被输入一个 CNN 模型。该模型通过识别复杂的模式从图像中提取重要信息。通过在标注数据集上进行训练，CNN 模型可预测 DR 或分类。方法：预处理方法通过去除噪音和波动来增强视网膜眼底图像的清晰度和差异。预处理阶段用于图片的归一化和非均匀亮度调整，以及对比度增强和噪声缓解，以消除噪声并提高后续处理阶段的精确率。结果：为了提高图像质量和减少噪声，图像处理工作中经常采用索贝尔、维纳、高斯和非局部均值滤波器等预处理技术。在特定任务中，非局部均值滤波器能产生更优越的结果；为提高性能，将其与 CNN 结合使用可能更有优势。在将处理过的图像提供给 CNN 进行预测之前，非局部均值滤波器可以帮助减少噪音，改善图像细节。结论：DR 分析的一种可行方法是使用索贝尔、维纳、高斯和非局部均值滤波器等图像预处理方法，然后使用 CNN 进行预测。这些预处理滤波器可以改善照片，为分析做好准备。经过预处理后，照片被送入 CNN 模型，该模型利用其发现复杂模式的能力从图像中提取重要元素。CNN 模型可通过在标记数据集上进行训练来预测 DR 或分类。通过将 CNN 预测与图像预处理过滤器相结合，可促进 DR 计算机辅助诊断系统的开发。这一策略可提高医护人员的工作效率，改善患者的治疗效果，减轻 DR 的负担。
{"title":"Prediction of Diabetic Retinopathy using Deep Learning with Preprocessing","authors":"S. Balaji, B. Karthik, D. Gokulakrishnan","doi":"10.4108/eetpht.10.5183","DOIUrl":"https://doi.org/10.4108/eetpht.10.5183","url":null,"abstract":"INTRODUCTION: When Diabetic Retinopathy (DR) is not identified promptly; it frequently results in sight impairment. To properly diagnose and treat DR, preprocessing of picture methods and precise prediction models are essential. With the help of numerous well-liked filters and a Deep CNN (Convolutional Neural Network) model, the comprehensive method for DR image preparation and prognosis presented in this research is described. Using the filters that focus boundaries and contours in the ocular pictures is the first step in the initial processing stage. This procedure tries to find anomalies linked to DR. By the usage of filters, the excellence of pictures can be developed and minimize disturbances, preserving critical information. The Deep CNN algorithm has been trained to generate forecasts on the cleaned retinal pictures following the phase of preprocessing. The filters efficiently eliminate interference without sacrificing vital data. Convolutional type layers, pooling type layers, and fully associated layers are used in the CNN framework, which was created especially for image categorization tasks, to acquire data and understand the relationships associated with DR. \u0000OBJECTIVES: Using image preprocessing techniques such as the Sobel, Wiener, Gaussian, and non-local mean filters is a promising approach for DR analysis. Then, predicting using a CNN completes the approach. These preprocessing filters enhance the images and prepare them for further examination. The pre-processed images are fed into a CNN model. The model extracts significant information from the images by identifying complex patterns. DR or classification may be predicted by the CNN model through training on a labeled dataset. \u0000METHODS: The Method Preprocessing is employed for enhancing the clarity and difference of retina fundus picture by removing noise and fluctuation. The preprocessing stage is utilized for the normalization of the pictures and non-uniform brightness adjustment in addition to contrast augmentation and noise mitigation to remove noises and improve the rate of precision of the subsequent processing stages. \u0000RESULTS: To improve image quality and reduce noise, preprocessing techniques including Sobel, Wiener, Gaussian, and non-local mean filters are frequently employed in image processing jobs. For a particular task, the non-local mean filter produces superior results; for enhanced performance, it may be advantageous to combine it with a CNN. Before supplying the processed images to the CNN for prediction, the non-local mean filter can assist reduce noise and improve image details. \u0000CONCLUSION: A promising method for DR analysis entails the use of image preprocessing methods such as the Sobel, Wiener, Gaussian, and non-local mean filters, followed by prediction using a CNN. These preprocessing filters improve the photos and get them ready for analysis. After being pre-processed, the photos are sent into a CNN model, which uses its capacity to discover intricate p","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"1 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140441467","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}
Balasubramaniam S, Arishma M, Satheesh Kumar K, Rajesh Kumar Dhanaraj
INTRODUCTION: The 2019 COVID-19 pandemic outbreak triggered a previously unseen global health crisis demanding accurate diagnostic solutions. Artificial Intelligence has emerged as a promising technology for COVID-19 diagnosis, offering rapid and reliable analysis of medical data. OBJECTIVES: This research paper presents a comprehensive review of various artificial intelligence methods applied for the diagnosis, aiming to assess their effectiveness in identifying cases, predicting disease progression and differentiating from other respiratory diseases. METHODS: The study covers a wide range of artificial intelligence methods and with application in analysing diverse data sources like chest x-rays, CT scans, clinical records and genomic sequences. The paper also explores the challenges and limitations in implementing AI -based diagnostic tools, including data availability and ethical considerations. CONCLUSION: Leveraging AI’s potential in healthcare can significantly enhance diagnostic efficiency crisis management as the pandemic evolves.
简介：2019 年 COVID-19 大流行的爆发引发了一场前所未见的全球健康危机，需要精确的诊断解决方案。人工智能已成为 COVID-19 诊断的一项前景广阔的技术，可对医疗数据进行快速、可靠的分析。目标：本研究论文全面综述了应用于诊断的各种人工智能方法，旨在评估这些方法在识别病例、预测疾病进展以及与其他呼吸道疾病区分方面的有效性。方法：本研究涵盖了多种人工智能方法，以及在分析胸部 X 光片、CT 扫描、临床记录和基因组序列等不同数据源方面的应用。本文还探讨了实施基于人工智能的诊断工具所面临的挑战和局限性，包括数据可用性和伦理方面的考虑。结论：随着大流行病的发展，利用人工智能在医疗保健领域的潜力可以大大提高诊断效率，加强危机管理。
{"title":"A Comprehensive Exploration of Artificial Intelligence Methods for COVID-19 Diagnosis","authors":"Balasubramaniam S, Arishma M, Satheesh Kumar K, Rajesh Kumar Dhanaraj","doi":"10.4108/eetpht.10.5174","DOIUrl":"https://doi.org/10.4108/eetpht.10.5174","url":null,"abstract":"INTRODUCTION: The 2019 COVID-19 pandemic outbreak triggered a previously unseen global health crisis demanding accurate diagnostic solutions. Artificial Intelligence has emerged as a promising technology for COVID-19 diagnosis, offering rapid and reliable analysis of medical data. \u0000OBJECTIVES: This research paper presents a comprehensive review of various artificial intelligence methods applied for the diagnosis, aiming to assess their effectiveness in identifying cases, predicting disease progression and differentiating from other respiratory diseases. \u0000METHODS: The study covers a wide range of artificial intelligence methods and with application in analysing diverse data sources like chest x-rays, CT scans, clinical records and genomic sequences. The paper also explores the challenges and limitations in implementing AI -based diagnostic tools, including data availability and ethical considerations. \u0000CONCLUSION: Leveraging AI’s potential in healthcare can significantly enhance diagnostic efficiency crisis management as the pandemic evolves.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"9 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140445031","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}
R. Athi Vaishnavi, P. Jegathesh, M. Jayasheela, K. Mahalakshmi
INTRODUCTION: In this study, we explore the intricate domain of Diabetic Foot Ulcers (DFU) through the development of a comprehensive framework that encompasses diverse operational scenarios. The focus lies on the identification and classification assessment of diabetic foot ulcers, the implementation of smart health management strategies, and the collection, analysis, and intelligent interpretation of data related to diabetic foot ulcers. The framework introduces an innovative approach to predicting diabetic foot ulcers and their key characteristics, offering a technical solution for forecasting. The exploration delves into various computational strategies designed for intelligent health analysis tailored to patients with diabetic foot ulcers. OBJECTIVES: The primary objective of this paper is to present a technical solution for forecasting diabetic foot ulcers, utilizing computational strategies for intelligent health analysis. METHODS: Techniques derived from social network analysis are employed to conduct this research, focusing on diverse computational strategies geared towards intelligent health analysis for patients with diabetic foot ulcers. The study highlights methodologies addressing the unique challenges posed by diabetic foot ulcers, with a central emphasis on the integration of Internet of Medical Things (IoMT) in prediction strategies. RESULTS: The main results of this paper include the proposal of IoMT-based computing strategies covering the entire spectrum of DFU analysis, such as localization, classification assessment, intelligent health management, and detection. The study also acknowledges the challenges faced by previous research, including low classification rates and elevated false alarm rates, and proposes automatic recognition approaches leveraging advanced machine learning techniques to enhance accuracy and efficacy. CONCLUSION: The proposed IoMT-based computing strategies present a significant advancement in addressing the challenges associated with predicting diabetic foot ulcers. The integration of advanced machine learning techniques demonstrates promise in improving accuracy and efficiency in diabetic foot ulcer localization, marking a positive stride towards overcoming existing limitations in previous research.
{"title":"A Survey on Impact of Internet of Medical Things Against Diabetic Foot Ulcer","authors":"R. Athi Vaishnavi, P. Jegathesh, M. Jayasheela, K. Mahalakshmi","doi":"10.4108/eetpht.10.5170","DOIUrl":"https://doi.org/10.4108/eetpht.10.5170","url":null,"abstract":"INTRODUCTION: In this study, we explore the intricate domain of Diabetic Foot Ulcers (DFU) through the development of a comprehensive framework that encompasses diverse operational scenarios. The focus lies on the identification and classification assessment of diabetic foot ulcers, the implementation of smart health management strategies, and the collection, analysis, and intelligent interpretation of data related to diabetic foot ulcers. The framework introduces an innovative approach to predicting diabetic foot ulcers and their key characteristics, offering a technical solution for forecasting. The exploration delves into various computational strategies designed for intelligent health analysis tailored to patients with diabetic foot ulcers. \u0000OBJECTIVES: The primary objective of this paper is to present a technical solution for forecasting diabetic foot ulcers, utilizing computational strategies for intelligent health analysis. \u0000METHODS: Techniques derived from social network analysis are employed to conduct this research, focusing on diverse computational strategies geared towards intelligent health analysis for patients with diabetic foot ulcers. The study highlights methodologies addressing the unique challenges posed by diabetic foot ulcers, with a central emphasis on the integration of Internet of Medical Things (IoMT) in prediction strategies. \u0000RESULTS: The main results of this paper include the proposal of IoMT-based computing strategies covering the entire spectrum of DFU analysis, such as localization, classification assessment, intelligent health management, and detection. The study also acknowledges the challenges faced by previous research, including low classification rates and elevated false alarm rates, and proposes automatic recognition approaches leveraging advanced machine learning techniques to enhance accuracy and efficacy. \u0000CONCLUSION: The proposed IoMT-based computing strategies present a significant advancement in addressing the challenges associated with predicting diabetic foot ulcers. The integration of advanced machine learning techniques demonstrates promise in improving accuracy and efficiency in diabetic foot ulcer localization, marking a positive stride towards overcoming existing limitations in previous research.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"9 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140445052","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}
Gamze Kose, Liliana Marmolejo-Saucedo, Miriam Rodríguez-Aguilar, Utku Kose
INTRODUCTION: Internet of Things (IoT) has been taking wide place in our daily lives. Among different solution ways in terms of IoT, wearables take a remarkable role because of their compact structures and the mobility. By using wearables, it is very easy to sense a person’s movements and gather characteristic data, which may be processed for desired outcomes if intelligent inferencing. As associated with this, wearables can be effectively used for health tourism operations. As wearables already proved their capabilities for healthcare-oriented applications, the perspective may be directed to health tourism purposes. In this way, positive contributions may be done in the context of not only patients’ well-being but also other actors such as health staff and tourism agencies.OBJECTIVES: Objective of this paper is to evaluate the potential of wearables in health tourism applications, provide a model suggestion, and evaluate it in the view of different actors enrolling in health tourism ecosystems. Within this objective, research targets were directed to the usage ways of wearables in health tourism, ensuring model structures as meeting with the digital transformation advantages, and gather some findings thanks to feedback by patients, health staff, and agencies.METHODS: The research firstly included some views on what is health tourism, how the IoT, mobile solutions as well as wearables may be included in the ecosystem. Following to that, the research ensured a model suggestion considering wearables and their connections to health tourism actors. Finally, the potentials of wearables and the model suggestion was evaluated by gathering feedback from potential / active health tourists, health staff, and agency staff. RESULTS: The research revealed that the recent advancements in wearables and the role of digital transformation affects health tourism. In this context, there is a great potential to track and manage states of all actors in a health tourism eco system. Thanks to data processing and digital systems, it is effective to rise fast and practical software applications for health tourism. In detail, this may be structured in a model where typical IoT and wearable interactions can be connected to sensors, databases, and the related users. According to the surveys done with potential / active health tourists, health staff, and agency staff, such a model has great effect to advance the health tourism.CONCLUSION: The research study shows positive perspectives for both present and future potentials of wearable and health tourism relation. It is remarkable that rapid advancements in IoT can trigger health tourism and the future of health tourism may be established over advanced applications including data and user-oriented relations.
{"title":"Wearables for Health Tourism: Perspectives and Model Suggestion","authors":"Gamze Kose, Liliana Marmolejo-Saucedo, Miriam Rodríguez-Aguilar, Utku Kose","doi":"10.4108/eetpht.10.4310","DOIUrl":"https://doi.org/10.4108/eetpht.10.4310","url":null,"abstract":"INTRODUCTION: Internet of Things (IoT) has been taking wide place in our daily lives. Among different solution ways in terms of IoT, wearables take a remarkable role because of their compact structures and the mobility. By using wearables, it is very easy to sense a person’s movements and gather characteristic data, which may be processed for desired outcomes if intelligent inferencing. As associated with this, wearables can be effectively used for health tourism operations. As wearables already proved their capabilities for healthcare-oriented applications, the perspective may be directed to health tourism purposes. In this way, positive contributions may be done in the context of not only patients’ well-being but also other actors such as health staff and tourism agencies.OBJECTIVES: Objective of this paper is to evaluate the potential of wearables in health tourism applications, provide a model suggestion, and evaluate it in the view of different actors enrolling in health tourism ecosystems. Within this objective, research targets were directed to the usage ways of wearables in health tourism, ensuring model structures as meeting with the digital transformation advantages, and gather some findings thanks to feedback by patients, health staff, and agencies.METHODS: The research firstly included some views on what is health tourism, how the IoT, mobile solutions as well as wearables may be included in the ecosystem. Following to that, the research ensured a model suggestion considering wearables and their connections to health tourism actors. Finally, the potentials of wearables and the model suggestion was evaluated by gathering feedback from potential / active health tourists, health staff, and agency staff. RESULTS: The research revealed that the recent advancements in wearables and the role of digital transformation affects health tourism. In this context, there is a great potential to track and manage states of all actors in a health tourism eco system. Thanks to data processing and digital systems, it is effective to rise fast and practical software applications for health tourism. In detail, this may be structured in a model where typical IoT and wearable interactions can be connected to sensors, databases, and the related users. According to the surveys done with potential / active health tourists, health staff, and agency staff, such a model has great effect to advance the health tourism.CONCLUSION: The research study shows positive perspectives for both present and future potentials of wearable and health tourism relation. It is remarkable that rapid advancements in IoT can trigger health tourism and the future of health tourism may be established over advanced applications including data and user-oriented relations.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"120 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139615106","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}
Lung Cancer, due to a lower survival rate, is a deadly disease as compared to other cancers. The prior determination of the lung cancer tends to increase the survival rate. Though there are numerous lung cancer detection techniques, they are all insufficient to detect accurate cancer due to variations in the intensity of the CT scan image. For more accuracy in segmentation of CT images, the proposed Elephant-Based Bald Eagle Optimization (EBEO) algorithm is used. This proposed research concentrates on developing a lung nodule detection technique based on Deep learning. To obtain an effective result, the segmentation process will be carried out using the proposed algorithm. Further, the proposed algorithm will be utilized to tune the hyper parameter of the deep learning classifier to increase detection accuracy. It is expected that the proposed state-of-art method will exceed all conventional methods in terms of detection accuracy due to the effectiveness of the proposed algorithm. This survey will be helpful for the healthcare research communities with sufficient knowledge to understand the concepts of the EBEO algorithm and the Deep Convolutional Neural Network for improving the overall human healthcare system.
{"title":"A Hybrid Optimization Approach for Pulmonary Nodules Segmentation and Classification using Deep CNN","authors":"Ajit Narendra Gedem, A. Rumale","doi":"10.4108/eetpht.10.4855","DOIUrl":"https://doi.org/10.4108/eetpht.10.4855","url":null,"abstract":"Lung Cancer, due to a lower survival rate, is a deadly disease as compared to other cancers. The prior determination of the lung cancer tends to increase the survival rate. Though there are numerous lung cancer detection techniques, they are all insufficient to detect accurate cancer due to variations in the intensity of the CT scan image. For more accuracy in segmentation of CT images, the proposed Elephant-Based Bald Eagle Optimization (EBEO) algorithm is used. This proposed research concentrates on developing a lung nodule detection technique based on Deep learning. To obtain an effective result, the segmentation process will be carried out using the proposed algorithm. Further, the proposed algorithm will be utilized to tune the hyper parameter of the deep learning classifier to increase detection accuracy. It is expected that the proposed state-of-art method will exceed all conventional methods in terms of detection accuracy due to the effectiveness of the proposed algorithm. This survey will be helpful for the healthcare research communities with sufficient knowledge to understand the concepts of the EBEO algorithm and the Deep Convolutional Neural Network for improving the overall human healthcare system.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":" 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139623104","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}
One kind of cancer that arises from an overabundance of white blood cells produced by the patient's bone marrow and lymph nodes is leukaemia. Since white blood cells are the primary source of immunity, or the body's defence, it is imperative to determine the type of leukocyte cell the patient has leukaemia from as soon as possible. Failure to do so could result in a more serious condition. Haematologists typically use a light microscope to examine the necessary cell traces in order to classify and identify the features of the cell cytoplasm or nucleus in order to diagnose leukaemia in a patient. One form of cancer is leukaemia, which develops when a patient's bone marrow and lymph nodes produce an excessive amount of white blood cells. It is vital to determine the type of leukocyte cell the patient has leukaemia from as soon as possible because postponing diagnosis can worsen the situation. Our white corpuscles are the primary source of immunity, which is the body's defence. In order to define and identify the features found in the cell cytoplasm or nucleus, hematopathologists typically use a light microscope to examine the necessary cell traces in order to diagnose leukaemia in patients.
{"title":"White Blood Cells Classification using CNN","authors":"Jinka Chandra Kiran, Beebi Naseeba, Abbaraju Sai Sathwik, Thadikala Prakash Badrinath Reddy, Kokkula Lokesh, Tatigunta Bhavi Teja Reddy, Nagendra Panini Challa","doi":"10.4108/eetpht.9.4852","DOIUrl":"https://doi.org/10.4108/eetpht.9.4852","url":null,"abstract":"One kind of cancer that arises from an overabundance of white blood cells produced by the patient's bone marrow and lymph nodes is leukaemia. Since white blood cells are the primary source of immunity, or the body's defence, it is imperative to determine the type of leukocyte cell the patient has leukaemia from as soon as possible. Failure to do so could result in a more serious condition. Haematologists typically use a light microscope to examine the necessary cell traces in order to classify and identify the features of the cell cytoplasm or nucleus in order to diagnose leukaemia in a patient. One form of cancer is leukaemia, which develops when a patient's bone marrow and lymph nodes produce an excessive amount of white blood cells. It is vital to determine the type of leukocyte cell the patient has leukaemia from as soon as possible because postponing diagnosis can worsen the situation. Our white corpuscles are the primary source of immunity, which is the body's defence. In order to define and identify the features found in the cell cytoplasm or nucleus, hematopathologists typically use a light microscope to examine the necessary cell traces in order to diagnose leukaemia in patients.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":" 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139622489","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}
Anshuman Sharma, Biswaroop Nath, Tejaswini Kar, D. Khasim
INTRODUCTION: Recent deep learning based image editing methods have achieved promising results for removing object in an image but fail to generate appreciable performance for removing large objects of complex nature, especially mask from facial images. Towards this goal the objective of this work is to remove mask objects in facial images. In this study, authors propose a novel approach for face completion using Generative Adversarial Networks (GANs) that utilize masked data. This technology can help in image restoration and preservation, thus enabling us to cherish those memories that are held dear to our hearts.OBJECTIVES: Train a GAN to learn the mapping from incomplete to complete face images by utilizing a masked input image.METHODS: The discriminator is trained to distinguish between face images and full ground truth images. Our results indicate that our technique generates high-quality, realistic facial images that are visually comparable to the ground truth and that it can generalise to new faces that were not encountered during training.RESULTS: Our findings indicate that GANs with masked inputs are a good approach for generating whole face images from partial or masked data.CONCLUSION: Our experimental findings show that our method produces facial images of great quality and realism that are visually equivalent to the actual thing. Our proposed approach can also be applied to fresh faces that weren’t seen. The performance can still be improved further using larger dataset. Also, further investigation into adversial attacks may help in improving performance. This technology can be further utilized for developing realtime mask removal software as well.
{"title":"Masked GANs for Face Completion: A Novel Deep Learning Approach","authors":"Anshuman Sharma, Biswaroop Nath, Tejaswini Kar, D. Khasim","doi":"10.4108/eetpht.9.4850","DOIUrl":"https://doi.org/10.4108/eetpht.9.4850","url":null,"abstract":"INTRODUCTION: Recent deep learning based image editing methods have achieved promising results for removing object in an image but fail to generate appreciable performance for removing large objects of complex nature, especially mask from facial images. Towards this goal the objective of this work is to remove mask objects in facial images. In this study, authors propose a novel approach for face completion using Generative Adversarial Networks (GANs) that utilize masked data. This technology can help in image restoration and preservation, thus enabling us to cherish those memories that are held dear to our hearts.OBJECTIVES: Train a GAN to learn the mapping from incomplete to complete face images by utilizing a masked input image.METHODS: The discriminator is trained to distinguish between face images and full ground truth images. Our results indicate that our technique generates high-quality, realistic facial images that are visually comparable to the ground truth and that it can generalise to new faces that were not encountered during training.RESULTS: Our findings indicate that GANs with masked inputs are a good approach for generating whole face images from partial or masked data.CONCLUSION: Our experimental findings show that our method produces facial images of great quality and realism that are visually equivalent to the actual thing. Our proposed approach can also be applied to fresh faces that weren’t seen. The performance can still be improved further using larger dataset. Also, further investigation into adversial attacks may help in improving performance. This technology can be further utilized for developing realtime mask removal software as well.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"14 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139529534","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}
In the healthcare industry artificial intelligence (AI) has become a disruptive technology that is revolutionizing patient care, diagnostics, and research. This abstract provides an overview of the main points and findings related to AI in healthcare exploring its advancements, applications, and ethical challenges. The rapid growth of AI technologies has led to remarkable improvements in healthcare. AI algorithms have demonstrated exceptional capabilities in analyzing number of patient data, enabling early disease detection, personalized treatment plans, and improved patient outcomes. Machine learning algorithms, such as deep learning and natural language processing, have been effectively employed to analyze medical images, predict disease progression, and support clinical decision-making. AI applications in healthcare span across various domains, including radiology, pathology, genomics, drug discovery, and patient monitoring. Telemedicine and AI-driven virtual health assistants have extended healthcare access to remote areas, empowering patients with self-care tools and enabling real-time communication with healthcare professionals. While it's undeniable that AI brings significant advantages to the field of healthcare, it's vital to emphasize the importance of ethical concerns. Additionally, ensuring that AI algorithms are transparent and interpretable is essential for establishing trust and promoting the responsible use of AI technology in clinical environments.
{"title":"Artificial Intelligence in Medical Filed","authors":"Iram Fatima, Veena Grover, Ihtiram Raza Khan, Naved Ahmad, Ambooj Yadav","doi":"10.4108/eetpht.9.4713","DOIUrl":"https://doi.org/10.4108/eetpht.9.4713","url":null,"abstract":"In the healthcare industry artificial intelligence (AI) has become a disruptive technology that is revolutionizing patient care, diagnostics, and research. This abstract provides an overview of the main points and findings related to AI in healthcare exploring its advancements, applications, and ethical challenges. The rapid growth of AI technologies has led to remarkable improvements in healthcare. AI algorithms have demonstrated exceptional capabilities in analyzing number of patient data, enabling early disease detection, personalized treatment plans, and improved patient outcomes. Machine learning algorithms, such as deep learning and natural language processing, have been effectively employed to analyze medical images, predict disease progression, and support clinical decision-making. AI applications in healthcare span across various domains, including radiology, pathology, genomics, drug discovery, and patient monitoring. Telemedicine and AI-driven virtual health assistants have extended healthcare access to remote areas, empowering patients with self-care tools and enabling real-time communication with healthcare professionals. While it's undeniable that AI brings significant advantages to the field of healthcare, it's vital to emphasize the importance of ethical concerns. Additionally, ensuring that AI algorithms are transparent and interpretable is essential for establishing trust and promoting the responsible use of AI technology in clinical environments.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"21 s2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139150187","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}
Artificial intelligence (AI) is transforming how we work and make choices, but it also poses ethical and societal issues including algorithmic discrimination and dehumanization. It is critical to take into account corporate culture, emotional intelligence, cooperation, communication, and constant learning when using AI systems in the workplace. It has been demonstrated that emotional intelligence increases AI adoption, efficacy, and performance across a variety of sectors. But ethical concerns and trouble making decisions are also important. Effective collaboration, communication, and corporate culture are crucial for successful AI adoption, and continuing learning and development are essential for enhancing decision-making abilities. AI ethics in the workplace necessitate a comprehensive strategy that considers both technical and non-technical aspects. This study looks at the benefits of emotional intelligence, moral concerns, effective stakeholder and IT specialist engagement, organisational culture, and potential threats of artificial intelligence (AI) in decision-making. The study underlines the value of continuous AI learning and development.
{"title":"Analyzing How AI And Emotional Intelligence Affect Indian IT Professional’s Decision-Making","authors":"Anita Shukla, Alka Algnihotri, Bhawna Singh","doi":"10.4108/eetpht.9.4654","DOIUrl":"https://doi.org/10.4108/eetpht.9.4654","url":null,"abstract":"Artificial intelligence (AI) is transforming how we work and make choices, but it also poses ethical and societal issues including algorithmic discrimination and dehumanization. It is critical to take into account corporate culture, emotional intelligence, cooperation, communication, and constant learning when using AI systems in the workplace. It has been demonstrated that emotional intelligence increases AI adoption, efficacy, and performance across a variety of sectors. But ethical concerns and trouble making decisions are also important. Effective collaboration, communication, and corporate culture are crucial for successful AI adoption, and continuing learning and development are essential for enhancing decision-making abilities. AI ethics in the workplace necessitate a comprehensive strategy that considers both technical and non-technical aspects. This study looks at the benefits of emotional intelligence, moral concerns, effective stakeholder and IT specialist engagement, organisational culture, and potential threats of artificial intelligence (AI) in decision-making. The study underlines the value of continuous AI learning and development.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"11 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139168695","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}
INTRODUCTION: A deep understanding of airborne pathogen transmission and mitigation efforts is crucial for designing effective health policies. Therefore, it is necessary to analyze research trends related to airborne pathogen transmission and mitigation strategies to identify the latest developments, especially concerning scientific knowledge. OBJECTIVES: The study was conducted to get a deeper understanding of research trends related to airborne transmission of pathogens.METHODS: Bibliometric analysis with the help of VOSviewer and RStudio was considered suitable for use in this study.RESULTS: Based on the research results, the topic of airborne pathogens is still a hot topic for research. 2021 is the year when the number of publications regarding airborne pathogens peaked, which is due to the covid 19 pandemic condition. Apart from that, this research also found research on the transmission and mitigation of airborne pathogens relatively less..CONCLUSION: The topic of airborne pathogens is still a hot topic for research.
{"title":"Research Trends on Airborne Pathogen Transmission and Mitigation","authors":"A. Nandiyanto, D. N. A. Husaeni, D. F. A. Husaeni","doi":"10.4108/eetpht.9.4633","DOIUrl":"https://doi.org/10.4108/eetpht.9.4633","url":null,"abstract":"INTRODUCTION: A deep understanding of airborne pathogen transmission and mitigation efforts is crucial for designing effective health policies. Therefore, it is necessary to analyze research trends related to airborne pathogen transmission and mitigation strategies to identify the latest developments, especially concerning scientific knowledge. OBJECTIVES: The study was conducted to get a deeper understanding of research trends related to airborne transmission of pathogens.METHODS: Bibliometric analysis with the help of VOSviewer and RStudio was considered suitable for use in this study.RESULTS: Based on the research results, the topic of airborne pathogens is still a hot topic for research. 2021 is the year when the number of publications regarding airborne pathogens peaked, which is due to the covid 19 pandemic condition. Apart from that, this research also found research on the transmission and mitigation of airborne pathogens relatively less..CONCLUSION: The topic of airborne pathogens is still a hot topic for research.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"55 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138965372","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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