EAI Endorsed Transactions on Pervasive Health and Technology最新文献

{"title":"An Effective analysis of brain tumor detection using deep learning","authors":"Yenumala Sankararao, Syed Khasim","doi":"10.4108/eetpht.10.5627","DOIUrl":"https://doi.org/10.4108/eetpht.10.5627","url":null,"abstract":"INTRODUCTION: Cancer remains a significant health concern, with early detection crucial for effective treatment. Brain tumors, in particular, require prompt diagnosis to improve patient outcomes. Computational models, specifically deep learning (DL), have emerged as powerful tools in medical image analysis, including the detection and classification of brain tumors. DL leverages multiple processing layers to represent data, enabling enhanced performance in various healthcare applications. \u0000OBJECTIVES: This paper aims to discuss key topics in DL relevant to the analysis of brain tumors, including segmentation, prediction, classification, and assessment. The primary objective is to employ magnetic resonance imaging (MRI) pictures for the identification and categorization of brain malignancies. By reviewing prior research and findings comprehensively, this study provides valuable insights for academics and professionals in deep learning seeking to contribute to brain tumor identification and classification. \u0000METHODS: The methodology involves a systematic review of existing literature on DL applications in brain tumor analysis, focusing on MRI imaging. Various DL techniques, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid models, are explored for their efficacy in tasks such as tumor segmentation, prediction of tumor characteristics, classification of tumor types, and assessment of treatment response. \u0000RESULTS: The review reveals significant advancements in DL-based approaches for brain tumor analysis, with promising results in segmentation accuracy, tumor subtype classification, and prediction of patient outcomes. Researchers have developed sophisticated DL architectures tailored to address the complexities of brain tumor imaging data, leading to improved diagnostic capabilities and treatment planning. \u0000CONCLUSION: Deep learning holds immense potential for revolutionizing the diagnosis and management of brain tumors through MRI-based analysis. This study underscores the importance of leveraging DL techniques for accurate and efficient brain tumor identification and classification. By synthesizing prior research and highlighting key findings, this paper provides valuable guidance for researchers and practitioners aiming to contribute to the field of medical image analysis and improve outcomes for patients with brain malignancies.","PeriodicalId":506478,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"6 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140747813","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}

{"title":"Predicting and Propagation of Diabetic Foot Infection by Deep Learning Model","authors":"Rajanish Kumar Kaushal, P. R. P. Pagidimalla, C. Nalini, Devendra Kumar","doi":"10.4108/eetpht.10.5614","DOIUrl":"https://doi.org/10.4108/eetpht.10.5614","url":null,"abstract":"INTRODUCTION: A deep learning model may be used to predict the occurrence of diabetic foot infections and to understand how these infections spread over time by using sophisticated machine learning methods. Untreated diabetic foot infections, a common diabetic complication, may have devastating effects. \u0000METHODOLOGY: One area where deep learning models—a kind of machine learning—shine is in healthcare, where they are well-suited to deal with data that contains intricate patterns and correlations. The metabolic illness of diabetes affects more individuals than any other.  Neuropathic and Ischemic ulcers are two types of foot ulcers that these issues may cause. Damage to the nerves and blood vessels is the primary cause of this ulcer. Numerous amputations and fatalities have resulted from these sores. There are millions of victims of this illness throughout the globe. The amputation of a human leg occurs once every 30 seconds. The precise anticipation of diabetic foot ulcers has the potential to significantly alleviate the substantial impact of amputation Therefore, it is crucial to correctly categorize foot ulcers and discover them as soon as possible for more effective treatment. \u0000RESULTS: An extensive literature review of classification methods, including decision trees, random forests, the M5 tree method, Random trees, neural network models, ZeroR, Naive Bayes, the Back Propagation Neural Network, Linear Regression model, and Deep Learning Algorithms is presented in this research with a primary emphasis on foot ulcer classification. Using the Kaggle dataset, these algorithms are ranked. In the end, it presents a comparison of different classifiers.","PeriodicalId":506478,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"56 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140754523","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}

{"title":"Influenza Diagnosis Deep Learning: Machine Learning Approach for Pharyngeal Image Infection","authors":"Chetan Chaudhari, Sapana Fegade, Sasanko Sekhar Gantayat, Kumari Jugnu, Vikash Sawan","doi":"10.4108/eetpht.10.5613","DOIUrl":"https://doi.org/10.4108/eetpht.10.5613","url":null,"abstract":"INTRODUCTION: Annual influenza epidemics and rare pandemics represent a significant global health risk. Since the upper respiratory tract is the primary target of influenza, a diagnosis of influenza illness might be made using deep learning applied to pictures of the pharynx. Using pharyngeal imaging data and clinical information, the researcher created a deep-learning model for influenza diagnosis. People who sought medical attention for flu-like symptoms were the subjects included.\u0000METHODOLOGY: The study created a diagnostic and predicting Artificial Intelligence (AI) method using deep learning techniques to forecast clinical data and pharyngeal pictures for PCR confirmation of influenza. The accuracy of the AI method as a diagnostic tool was measured during the validation process. The extra research evaluated the AI model's diagnosis accuracy to that of three human doctors and explained the methodology using high-impact heat maps. In the training stage, a cohort of 8,000 patients was recruited from 70 hospitals. Subsequently, a subset of 700 patients, including 300 individuals with PCR-confirmed influenza, was selected from 15 hospitals during the validation stage.\u0000RESULTS: The AI model exhibited an operating receiver curve with an area of 1.01, surpassing the performance of three doctors by achieving a sensitivity of 80% and a specificity of 80%. The significance of heat maps lies in their ability to provide valuable insights. In AI models, particular attention is often directed towards analyzing follicles on the posterior pharynx wall. Researchers introduced a novel artificial intelligence model that can assist medical professionals in swiftly diagnosing influenza based on pharyngeal images.","PeriodicalId":506478,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"188 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140751603","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}

{"title":"A Comparative Analysis using various algorithm Approaches to Enhance Heart Disease Prognosis","authors":"Anuraag Ramineni, Rishita Konda, Jayashree J, Deepak Sannapareddy, Saketh Konduri","doi":"10.4108/eetpht.10.5615","DOIUrl":"https://doi.org/10.4108/eetpht.10.5615","url":null,"abstract":"INTRODUCTION: Modern advancements in technology and data science have propelled the healthcare industry towards developing more accurate disease prognostic prediction models. Heart disease, being a leading cause of mortality globally, is a critical area of focus. This study delves into enhancing heart disease prognosis through a comprehensive exploration of various algorithmic approaches. \u0000OBJECTIVES: The objective of this paper is to compare and analyze different algorithmic techniques to improve heart disease prognosis using a dataset comprising data from over thirty thousand individuals obtained through Kaggle. \u0000METHODS: Techniques derived from social network analysis are employed to conduct this research. Data preprocessing, feature engineering, algorithm selection (including Stochastic Gradient Descent, AdaBoosting, Support Vector Machine, and Naive Bayes), hyperparameter tuning, model evaluation, and visualization are part of the systematic research process. \u0000RESULTS: The main results obtained in this paper include the identification of Naive Bayes as the most effective model for heart disease prognosis, followed by AdaBoosting, SVM, and Stochastic Gradient Descent. Performance evaluation metrics such as AUC, CA, F1, Precision, and Recall demonstrate the efficacy of these models. \u0000CONCLUSION: This research contributes to improving heart disease prognosis by leveraging algorithmic techniques and thorough analysis. The study envisions integrating the developed model into healthcare systems for widespread access to accurate heart disease prediction, with future plans to enhance data collection and model improvement for better outcomes.","PeriodicalId":506478,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"35 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140753745","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}

{"title":"A Review on the Importance of Machine Learning in the Health-Care Domain","authors":"Tarandeep Kaur Bhatia, Prerana, Sudhanshu Singh, Navya Saluja, Yoshudeep Singh Gour","doi":"10.4108/eetpht.10.5330","DOIUrl":"https://doi.org/10.4108/eetpht.10.5330","url":null,"abstract":"INTRODUCTION: An analysis of the convergence of blockchain and artificial intelligence (AI) technology demonstrates how these technologies can work together to revolutionize data management across a wide range of industries with their synergistic potential. \u0000OBJECTIVES: This paper discusses the integration of blockchain and artificial intelligence, the authors present an innovative framework that takes advantage of their strengths. As a result of blockchain's immutability and transparency, data can be securely stored and shared within this framework, making it ideal for sectors such as healthcare, finance, and supply chain. \u0000METHODS: To begin with, the paper discusses blockchain and artificial intelligence individually, emphasizing their respective advantages in decentralized data storage and intelligent decision-making. Blockchain-AI convergence is inevitable as both deal with data and value. \u0000RESULTS: As a result, the research paper highlights how blockchain and AI technologies can be transformed into transformative technologies. \u0000CONCLUSION: Using the synergistic framework presented in this paper, data management can be made more secure, transparent, and intelligent, with implications that go beyond traditional industries into emerging fields like the Internet of Things (IoT) and smart cities.","PeriodicalId":506478,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"35 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140077479","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}

{"title":"A Comprehensive Study on Mental Illness Through Speech and EEG Using Artificial Intelligence","authors":"Sanjana Bhat, Reeja S R","doi":"10.4108/eetpht.10.5328","DOIUrl":"https://doi.org/10.4108/eetpht.10.5328","url":null,"abstract":"  \u0000A typical mental ailment is depression that considerably harms an individual's everyday activities as well as their mental health. In light of the fact that mental health is one of the biggest problems facing society, researchers have been looking into several strategies for efficiently identifying depression. Mental illness can now be identified through speech analysis thanks to modern artificial intelligence. The speech aids in classifying a patient's mental health status, which could benefit their new study. For the purpose of identifying depression or any other emotion or mood in an individual, a number of past studies based on machine learning and artificial intelligence are being studied. The study also examines the effectiveness of facial expression, photos, emotional chatbots, and texts in identifying a person's emotions. Naive-Bayes, Support Vector Machines (SVM), Linear Support Vectors, Logistic Regression, etc. are ML approaches from text processing. Artificial Neural Network (ANN) is a sort of artificial intelligence method used to extract information from photos and classify them in order to recognise emotions from facial expressions.","PeriodicalId":506478,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"74 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140078048","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}

{"title":"An Innovative approach to Improve the Quality of Pharmaceuticals approach using Cloud Computing","authors":"N. Vijayaraj, D. Rajalakshmi, P. S. Immaculate, B. Sathianarayani, S. Rajeswari, S. Gomathi","doi":"10.4108/eetpht.10.5270","DOIUrl":"https://doi.org/10.4108/eetpht.10.5270","url":null,"abstract":"INTRODUCTION: Pharmaceuticals evolve alongside advancing technology driven by ongoing research and pharmaceutical companies’ production of new medications. Ongoing research and adjustment are necessary for various aspects of the pharmaceutical sector, such as patient understanding, drug testing, manufacturing, and communication of complex concepts through technology. \u0000OBJECTIVES: This paper discusses the intersection of cloud computing, technological advancements, and healthcare applications. \u0000METHODS: The Azure Cloud facilitates data processing, customer and patient engagement, employee and care team empowerment, clinical and operational optimisation, and healthcare digital transformation in the pharmaceutical industry. The integration of Microsoft Azure cloud technologies inside the pharmaceutical industry is examined in this research. RESULTS: Analysing how Internet of Things (IoT) sensors and the Industrial Internet of Things (IIoT) are used in pharmaceutical manufacturing and logistics, benefits in drug research, production monitoring and supply chain optimisation are highlighted. \u0000CONCLUSION: Cloud computing's potential to facilitate General Data Protection Regulation compliance, improve security, and promote innovation is explored.","PeriodicalId":506478,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"111 29","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140089568","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}

{"title":"IMU-Based Approach to Detect Spastic Cerebral Palsy in Infants at Early Stages","authors":"N. Sukhadia, P. Kamboj","doi":"10.4108/eetpht.10.5258","DOIUrl":"https://doi.org/10.4108/eetpht.10.5258","url":null,"abstract":"INTRODUCTION: Cerebral Palsy (CP) is a non-progressive neurological disorder affecting muscle control in early childhood, leading to permanent alterations in body posture and movement. Early identification is crucial for accurate diagnosis and therapy-based interventions. In recent years, an automated monitoring system has been developed to facilitate the health assessment of infants, enabling early recognition of neurological dysfunctions in high-risk infants. However, the interpretation of these assessments lacks standardization and is subject to examiner bias. \u0000OBJECTIVES: Many infants with CP exhibit increased tonic stretch reflexes due to Upper Motor Neuron Syndrome (UMNS), resulting from motor neuron damage that disrupts muscle signalling. \u0000METHOD: To detect abnormal muscle reactions, our team employed an Inertial Measurement Unit (IMU) sensor, comprising three tri-axial sensors (accelerometer, gyroscope, magnetometer) that capture movement data continuously and unobtrusively. IMU sensors are compact, cost-effective, and have low processing requirements, requiring attachment to the infant's body to measure inter-body part angles. Our team analyzed muscle activity and posture using IMU sensors, collecting tri-axial data from 43 infants in real-time. Additional factors like age, stride length, and leg length were incorporated into the dataset. \u0000RESULTS: Our team has applied various supervised machine learning approaches to predict CP in infants due to the limited dataset size, validating models through k-fold cross-validation. Among the models, Naive Bayes (NB) outperformed Logistic Regression (LR), Decision Tree (DT), Linear Discriminant Analysis (LDA), k-Nearest Neighbors (kNN), and Support Vector Machine (SVM), achieving an accuracy of 88%. CONCLUSION: This research contributes to the early detection and intervention of CP in infants, potentially improving their long-term outcomes.","PeriodicalId":506478,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"32 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140084319","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}

{"title":"Lung Cancer Detection in CT Images Using Deep Learning Techniques: A Survey Review","authors":"C. Usharani, B. Revathi, A. Selvapandian, S. K. Kezial Elizabeth","doi":"10.4108/eetpht.10.5265","DOIUrl":"https://doi.org/10.4108/eetpht.10.5265","url":null,"abstract":"INTRODUCTION: The Computed Tomography (CT) imaging-based Lung cancer detection is crucial for early diagnosis. This survey paper presents an overview of the techniques and advancements in CT-based lung cancer detection. It covers the fundamentals of CT imaging, including principles, types, and protocols. \u0000OBJECTIVES: The paper explores image processing techniques for pre-processing, such as noise reduction, enhancement, and segmentation. \u0000METHODS: Additionally, it discusses feature extraction methods, including shape, texture, and intensity-based features, as well as Deep Learning (DL) and Machine Learning (ML) methods for automated classification. \u0000RESULTS: Computerised systems and their integration is examined with CT imaging along with performance evaluation metrics. The survey concludes by addressing challenges, limitations, and future directions. The imaging modalities and artificial intelligence techniques are used to improve lung cancer detection. \u0000CONCLUSION: This comprehensive survey aims to provide a concise understanding of CT-based lung cancer detection for researchers and healthcare professionals.","PeriodicalId":506478,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"109 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140088809","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}

{"title":"Comparative Analysis of CNN and Different R-CNN based Model for Prediction of Alzheimer’s Disease","authors":"S. Roobini, M. S. Kavitha, S. Karthik","doi":"10.4108/eetpht.10.5267","DOIUrl":"https://doi.org/10.4108/eetpht.10.5267","url":null,"abstract":"INTRODUCTION: Medical images still need to be examined by medical personnel, which is a prolonged and vulnerable progression. The dataset used included 4 classes of 6400 training and test MRI images each and was collected from Kaggle such as cognitively normal (CN), Mild Cognitive Impairment stage (MCI), moderate cognitive impairment (Moderate MCI), and Severe stage of cognitive impairment (AD). \u0000OBJECTIVES: There was a glaring underrepresentation of the Alzheimer Disease (AD) class. The accuracy and effectiveness of diagnoses can be improved with the use of neural network models. \u0000METHODS: In order to establish which CNN-based algorithm performed the multi-class categorization of the AD patient's brain MRI images most accurately. Thus, examine the effectiveness of the popular CNN-based algorithms like Convolutional Neural Network (CNN), Region-based CNN (R-CNN), Fast R-CNN, and Faster R-CNN. \u0000RESULTS:  On the confusion matrix, R-CNN performed the best. \u0000CONCLUSION: R-CNN is quick and offers a high precision of 98.67% with a low erroneous measure of 0.0133, as shown in the research.","PeriodicalId":506478,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"122 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140090578","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}