Tlou Maggie Masenya, F. Ssekitto, Sarah Kaddu, Sam Simati
This article examined the management of electronic health records in virtual health environments using rocket health as a case study. The specific objectives of the study were to determine the healthcare services provided at rocket health; examine the electronic health records management practices adhered to at rocket health; and determine the inhibitors to effective electronic health records management at rocket health. A case study with a mixed-methods research approach was used. Data was collected using questionnaires, document reviews and structured interviews. The study finds that rocket health provided a range of healthcare services encompassing telehealth, pharmacy, last mile delivery, and an online store. These services predominantly operated in a digital format, resulting in the generation of electronic health records (EHRs), and therefore to capture and maintain these EHRs from multiple service points, rocket health implemented a cloud-based system.
{"title":"Management of Electronic Health Records in Virtual Health Environments: The Case of Rocket Health in Uganda","authors":"Tlou Maggie Masenya, F. Ssekitto, Sarah Kaddu, Sam Simati","doi":"10.4018/ijhisi.342089","DOIUrl":"https://doi.org/10.4018/ijhisi.342089","url":null,"abstract":"This article examined the management of electronic health records in virtual health environments using rocket health as a case study. The specific objectives of the study were to determine the healthcare services provided at rocket health; examine the electronic health records management practices adhered to at rocket health; and determine the inhibitors to effective electronic health records management at rocket health. A case study with a mixed-methods research approach was used. Data was collected using questionnaires, document reviews and structured interviews. The study finds that rocket health provided a range of healthcare services encompassing telehealth, pharmacy, last mile delivery, and an online store. These services predominantly operated in a digital format, resulting in the generation of electronic health records (EHRs), and therefore to capture and maintain these EHRs from multiple service points, rocket health implemented a cloud-based system.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":" 41","pages":"1-17"},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140691395","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 this article, the outpatient volume, hospitalization income and drug demand in hospital management are taken as the research objects, and a neural network combined prediction model is established to predict the outpatient volume with the fitting prediction results of cubic polynomial regression model and grey model as the input of the network and the actual statistical outpatient volume as the output. Lasso variable selection method is used to determine the main indexes affecting the income of inpatients in hospital, and a prediction model combining grey prediction and artificial neural network is established to predict the income of inpatients in hospital. By studying the key characteristics of hospital drug demand, BP neural network, RBF neural network and GRNN generalized regression neural network are selected to predict the drug demand. By solving the quadratic programming problem and according to the weight rules, a combination forecasting model based on neural network is established to predict the drug demand, and the accuracy and stability of the model are evaluated.
本文以医院管理中的门诊量、住院收入和药品需求为研究对象，以三次多项式回归模型和灰色模型的拟合预测结果为网络输入，以实际统计的门诊量为输出，建立了神经网络组合预测模型对门诊量进行预测。采用拉索变量选择法确定影响住院患者收入的主要指标，建立灰色预测与人工神经网络相结合的预测模型，预测住院患者收入。通过研究医院药品需求的主要特征，选择 BP 神经网络、RBF 神经网络和 GRNN 广义回归神经网络对药品需求进行预测。通过求解二次编程问题并根据权重规则，建立了基于神经网络的组合预测模型来预测药品需求，并对模型的准确性和稳定性进行了评估。
{"title":"Hospital Management Practice of Combined Prediction Method Based on Neural Network","authors":"Qi Yang","doi":"10.4018/ijhisi.342091","DOIUrl":"https://doi.org/10.4018/ijhisi.342091","url":null,"abstract":"In this article, the outpatient volume, hospitalization income and drug demand in hospital management are taken as the research objects, and a neural network combined prediction model is established to predict the outpatient volume with the fitting prediction results of cubic polynomial regression model and grey model as the input of the network and the actual statistical outpatient volume as the output. Lasso variable selection method is used to determine the main indexes affecting the income of inpatients in hospital, and a prediction model combining grey prediction and artificial neural network is established to predict the income of inpatients in hospital. By studying the key characteristics of hospital drug demand, BP neural network, RBF neural network and GRNN generalized regression neural network are selected to predict the drug demand. By solving the quadratic programming problem and according to the weight rules, a combination forecasting model based on neural network is established to predict the drug demand, and the accuracy and stability of the model are evaluated.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"52 2","pages":"1-13"},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140726402","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}
Richard Kumi, Iris Reychav, J. Azuri, R. Sabherwal
The purpose of the study is to investigate patient-physician interactions during a clinical encounter to ascertain the impact of tablet computing on physician satisfaction during a clinical encounter. This study was conducted at a primary care clinic, and the physicians who participated could use a tablet during their clinical encounters. The authors compared satisfaction between physicians who used the tablet during a clinical encounter and those who did not using data from 122 clinical encounters involving 82 patients. The results indicate that physicians who used the tablet during clinical encounters are more satisfied than those who did not. Additionally, there was a meaning difference in satisfaction between physicians who used the tablet to educate patients and share information than those who did not. HITs have potential benefits, but they also come with risks. To effectively manage the risks and benefits of HITs, healthcare providers should be deliberate and strategic in the implementation of HITs.
{"title":"Tablet in the Consultation Room and Physician Satisfaction","authors":"Richard Kumi, Iris Reychav, J. Azuri, R. Sabherwal","doi":"10.4018/ijhisi.318445","DOIUrl":"https://doi.org/10.4018/ijhisi.318445","url":null,"abstract":"The purpose of the study is to investigate patient-physician interactions during a clinical encounter to ascertain the impact of tablet computing on physician satisfaction during a clinical encounter. This study was conducted at a primary care clinic, and the physicians who participated could use a tablet during their clinical encounters. The authors compared satisfaction between physicians who used the tablet during a clinical encounter and those who did not using data from 122 clinical encounters involving 82 patients. The results indicate that physicians who used the tablet during clinical encounters are more satisfied than those who did not. Additionally, there was a meaning difference in satisfaction between physicians who used the tablet to educate patients and share information than those who did not. HITs have potential benefits, but they also come with risks. To effectively manage the risks and benefits of HITs, healthcare providers should be deliberate and strategic in the implementation of HITs.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132936177","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}
Telemedicine's growth during the COVID-19 pandemic exposed digital and health disparities in U.S. communities. Public health advocates suggest disparities in healthcare access may be mitigated through free or low-cost broadband. However, prior research shows that many factors influence patient adoption of information technologies; therefore, increasing access to broadband alone is insufficient. This paper advances a patient-centered model of telemedicine (TM) adoption supported by qualitative interview data. The model illustrates that patient adoption of TM is driven by a complex sociotechnical system comprised of technology factors, structural factors underlying the provider's provision of TM, and individual patient factors. Findings highlight the importance of the physical place of the TM visit, the need for experienced TM healthcare workers and technology support for patients, the impact of provider-mandated technology on task-technology fit (TTF), and the strength of the patient-provider relationship. These factors affect patient perceptions of TTF and ultimately TM adoption.
{"title":"Digital Disparities in Patient Adoption of Telemedicine: A Qualitative Analysis of the Patient Experience","authors":"Alissa M. Dickey, M. Wasko","doi":"10.4018/ijhisi.318043","DOIUrl":"https://doi.org/10.4018/ijhisi.318043","url":null,"abstract":"Telemedicine's growth during the COVID-19 pandemic exposed digital and health disparities in U.S. communities. Public health advocates suggest disparities in healthcare access may be mitigated through free or low-cost broadband. However, prior research shows that many factors influence patient adoption of information technologies; therefore, increasing access to broadband alone is insufficient. This paper advances a patient-centered model of telemedicine (TM) adoption supported by qualitative interview data. The model illustrates that patient adoption of TM is driven by a complex sociotechnical system comprised of technology factors, structural factors underlying the provider's provision of TM, and individual patient factors. Findings highlight the importance of the physical place of the TM visit, the need for experienced TM healthcare workers and technology support for patients, the impact of provider-mandated technology on task-technology fit (TTF), and the strength of the patient-provider relationship. These factors affect patient perceptions of TTF and ultimately TM adoption.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134290396","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}
Pub Date : 2022-04-01DOI: 10.4018/ijhisi.20220401.oa1
R. Gupta, Nilesh Kunhare, R. K. Pateriya, Nikhlesh Pathik
The novel Covid-19 is one of the leading cause of death worldwide in the year 2020 and declared as a pandemic by world health organization (WHO). This virus affecting all countries across the world and 5 lakh people die as of June 2020 due to Covid-19. Due to the highly contagious nature, early detection of this virus plays a vital role to break Covid chain. Recent studies done by China says that chest CT and X-Ray image may be used as a preliminary test for Covid detection. Deep learning-based CNN model can use to detect Coronavirus automatically from the chest X-rays images. This paper proposed a transfer learning-based approach to detect Covid disease. Due to the less number of Covid chest images, we are using a pre-trained model to classify X-ray images into Covid and Normal class. This paper presents the comparative study of a various pre-trained model like VGGNet-19, ResNet50 and Inception_ResNet_V2. Experiment results show that Inception_ResNet_V2 gives the better result as compare to VGGNet and ResNet model with training and test accuracy of 99.26 and 94, respectively.
{"title":"A Deep Neural Network for Detecting Coronavirus Disease Using Chest X-Ray Images","authors":"R. Gupta, Nilesh Kunhare, R. K. Pateriya, Nikhlesh Pathik","doi":"10.4018/ijhisi.20220401.oa1","DOIUrl":"https://doi.org/10.4018/ijhisi.20220401.oa1","url":null,"abstract":"The novel Covid-19 is one of the leading cause of death worldwide in the year 2020 and declared as a pandemic by world health organization (WHO). This virus affecting all countries across the world and 5 lakh people die as of June 2020 due to Covid-19. Due to the highly contagious nature, early detection of this virus plays a vital role to break Covid chain. Recent studies done by China says that chest CT and X-Ray image may be used as a preliminary test for Covid detection. Deep learning-based CNN model can use to detect Coronavirus automatically from the chest X-rays images. This paper proposed a transfer learning-based approach to detect Covid disease. Due to the less number of Covid chest images, we are using a pre-trained model to classify X-ray images into Covid and Normal class. This paper presents the comparative study of a various pre-trained model like VGGNet-19, ResNet50 and Inception_ResNet_V2. Experiment results show that Inception_ResNet_V2 gives the better result as compare to VGGNet and ResNet model with training and test accuracy of 99.26 and 94, respectively.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126170048","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}
Today, adults are use social media to seek health information. Evidence suggests that hospitals using Instagram reported better patient engagement and in turn increased profit and reputation. Yet, little is known about how public and private hospitals are leveraging Instagram. This study aims to analyze the presence of hospitals on Instagram using Kuwait as a case study. Hospitals were identified using the Ministry of Health’s website and Instagram. Posts collected from 7 odd months were analyzed using the Constant Comparison method. A total of 3,439 posts were distributed across six categories: Health advice & education, operations & services, current events, hospital community, seasonal occasions, and trivia. Public and private hospitals differed in their activity on Instagram in terms of health topics covered, post categories, and interactions. Hospitals should improve their presence on Instagram to promote healthy lifestyles, augment public health campaigns, and be a source of reliable and accessible health information online.
{"title":"Evaluating the Presence of Hospitals on Social Media: An Analytical Study of Private and Public Hospital Instagram Accounts in the State of Kuwait","authors":"Anwar F. AlHussainan, Zahraa Jasem, Dari Alhuwail","doi":"10.4018/ijhisi.299954","DOIUrl":"https://doi.org/10.4018/ijhisi.299954","url":null,"abstract":"Today, adults are use social media to seek health information. Evidence suggests that hospitals using Instagram reported better patient engagement and in turn increased profit and reputation. Yet, little is known about how public and private hospitals are leveraging Instagram. This study aims to analyze the presence of hospitals on Instagram using Kuwait as a case study. Hospitals were identified using the Ministry of Health’s website and Instagram. Posts collected from 7 odd months were analyzed using the Constant Comparison method. A total of 3,439 posts were distributed across six categories: Health advice & education, operations & services, current events, hospital community, seasonal occasions, and trivia. Public and private hospitals differed in their activity on Instagram in terms of health topics covered, post categories, and interactions. Hospitals should improve their presence on Instagram to promote healthy lifestyles, augment public health campaigns, and be a source of reliable and accessible health information online.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128779330","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}
Intelligent predictive systems are showing a greater level of accuracy and effectiveness in early detection of critical diseases like cancer and liver and lung disease.Predictive models assist medical practitioners in identifying the diseases based on symptoms and health indicators like hormone,enzymes,age,bloodcounts,etc.This study proposes a framework to use classification models to accurately detect chronic liver disease by enhancing the prediction accuracy through cutting-edge analytics techniques.The article proposes an enhanced framework on the original study by Ramana et al. (2011).It uses evaluation measures like Precision and Balanced Accuracy to choose the most efficient classification algorithm in INDIA and USA patient datasets using various factors like enzymes,age,etc.Using Youden’s Index, individual thresholds for each model were identified to increase the power of sensitivity and specificity.A framework is proposed for highly accurate automated disease detection in the medical industry,and it helps in strategizing preventive measures for patients with liver diseases.
智能预测系统在癌症、肝脏和肺部疾病等重大疾病的早期检测中显示出更高的准确性和有效性。预测模型帮助医生根据症状和健康指标(如激素、酶、年龄、血细胞计数等)识别疾病。本研究提出了一个使用分类模型的框架，通过尖端的分析技术提高预测精度，以准确检测慢性肝病。本文在Ramana et al.(2011)的原始研究基础上提出了一个增强的框架。它使用精度和平衡精度等评估措施来选择印度和美国患者数据集中最有效的分类算法，使用各种因素，如酶，年龄等。使用约登指数，确定每个模型的单独阈值，以提高灵敏度和特异性。提出了一种医疗行业中高度精确的自动化疾病检测框架，它有助于肝病患者制定预防措施。
{"title":"Liver Disease Detection: Evaluation of Machine Learning Algorithms Performances With Optimal Thresholds","authors":"Aritra Pan, Shameek Mukhopadhyay, S. Samanta","doi":"10.4018/ijhisi.299956","DOIUrl":"https://doi.org/10.4018/ijhisi.299956","url":null,"abstract":"Intelligent predictive systems are showing a greater level of accuracy and effectiveness in early detection of critical diseases like cancer and liver and lung disease.Predictive models assist medical practitioners in identifying the diseases based on symptoms and health indicators like hormone,enzymes,age,bloodcounts,etc.This study proposes a framework to use classification models to accurately detect chronic liver disease by enhancing the prediction accuracy through cutting-edge analytics techniques.The article proposes an enhanced framework on the original study by Ramana et al. (2011).It uses evaluation measures like Precision and Balanced Accuracy to choose the most efficient classification algorithm in INDIA and USA patient datasets using various factors like enzymes,age,etc.Using Youden’s Index, individual thresholds for each model were identified to increase the power of sensitivity and specificity.A framework is proposed for highly accurate automated disease detection in the medical industry,and it helps in strategizing preventive measures for patients with liver diseases.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129018227","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}
D. Tarenskeen, R. V. D. Wetering, R. Bakker, S. Brinkkemper
Modern healthcare organizations try to leverage their IT infrastructures to enhance the efficiency of processes and the quality of patient services. The flexibility of the IT infrastructure is a critical factor in the process of establishing strategic and operational value. The authors examine how applied principles of Conceptual Independence (CI) in information systems (IS) influence the flexibility of IT infrastructures. Furthermore, it is presumed that IT outsourcing plays a role in IT flexibility. The second question asks whether IT outsourcing configurations change when CI has been applied or not. Quantitative and qualitative data have been collected in 9 mental healthcare organizations. Findings – based on integration of the data with a mixed-method approach - suggest that the healthcare organizations that apply the principles of CI are better equipped to adapt their IT infrastructure to changing demands, requests and needs. Likewise, results suggest that they have changed the government of IT outsourcing thereby increasing IT flexibility even further.
{"title":"Investigating the Impact of Outsourcing on IT Flexibility: The Conceptual Independence Perspective","authors":"D. Tarenskeen, R. V. D. Wetering, R. Bakker, S. Brinkkemper","doi":"10.4018/ijhisi.299955","DOIUrl":"https://doi.org/10.4018/ijhisi.299955","url":null,"abstract":"Modern healthcare organizations try to leverage their IT infrastructures to enhance the efficiency of processes and the quality of patient services. The flexibility of the IT infrastructure is a critical factor in the process of establishing strategic and operational value. The authors examine how applied principles of Conceptual Independence (CI) in information systems (IS) influence the flexibility of IT infrastructures. Furthermore, it is presumed that IT outsourcing plays a role in IT flexibility. The second question asks whether IT outsourcing configurations change when CI has been applied or not. Quantitative and qualitative data have been collected in 9 mental healthcare organizations. Findings – based on integration of the data with a mixed-method approach - suggest that the healthcare organizations that apply the principles of CI are better equipped to adapt their IT infrastructure to changing demands, requests and needs. Likewise, results suggest that they have changed the government of IT outsourcing thereby increasing IT flexibility even further.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"352 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133463396","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 research was aimed to extract association rules on the morbidity and mortality of corona virus disease 2019 (COVID-19). The dataset has four attributes that determine morbidity and mortality; including Confirmed Cases, New Cases, Deaths, and New Deaths. The dataset was obtained as of 2nd April, 2020 from the WHO website and converted to transaction format. The Apriori algorithm was then deployed to extract association rules on these attributes. Six rules were extracted: Rule 1. {Deaths, NewDeaths}=>{NewCases}, Rule 2. {ConfCases, NewDeaths}=>{NewCases}, Rule 3. {ConfCases, Deaths}=>{NewCases}, Rule 4. {Deaths, NewCases}=>{NewDeaths}, Rule 5. {ConfCases, Deaths}=>{NewDeaths}, Rule 6. {ConfCases, NewCases}=>{NewDeaths}, with confidence 0.96, 0.96, 0.86, 0.66, 0.59, 0.51 respectively. These rules provide useful information that is vital on how to curtail further spread and deaths from the virus, both in areas where the pandemic is already ravaging and in areas yet to experience the outbreak.
{"title":"Association Rules Extraction From the Coronavirus Disease 2019: Attributes on Morbidity and Mortality","authors":"D. Atsa’am, R. Wario","doi":"10.4018/ijhisi.302652","DOIUrl":"https://doi.org/10.4018/ijhisi.302652","url":null,"abstract":"This research was aimed to extract association rules on the morbidity and mortality of corona virus disease 2019 (COVID-19). The dataset has four attributes that determine morbidity and mortality; including Confirmed Cases, New Cases, Deaths, and New Deaths. The dataset was obtained as of 2nd April, 2020 from the WHO website and converted to transaction format. The Apriori algorithm was then deployed to extract association rules on these attributes. Six rules were extracted: Rule 1. {Deaths, NewDeaths}=>{NewCases}, Rule 2. {ConfCases, NewDeaths}=>{NewCases}, Rule 3. {ConfCases, Deaths}=>{NewCases}, Rule 4. {Deaths, NewCases}=>{NewDeaths}, Rule 5. {ConfCases, Deaths}=>{NewDeaths}, Rule 6. {ConfCases, NewCases}=>{NewDeaths}, with confidence 0.96, 0.96, 0.86, 0.66, 0.59, 0.51 respectively. These rules provide useful information that is vital on how to curtail further spread and deaths from the virus, both in areas where the pandemic is already ravaging and in areas yet to experience the outbreak.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121851983","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}
Venkata Satya Vivek Tammineedi, C. Raju, D. GirishKumar, Venkateswarlu Yalla
Mammogram segmentation utilizing multi-region of intrigue is a standout amongst the most rising exploration territory in the medical image analysis. The steps engaged with the research are grouped into two kinds: 1) segmentation of mammogram images and 2) extraction of texture features from mammogram images. To overcome these difficulties, a compelling technique is proposed in this paper that comprises of three phases. In the principal arrangement, mammogram images from INbreast database are selected and improved utilizing Laplacian filtering. At that point, the pre-processed mammogram images are utilized for segmentation utilizing modified adaptively regularized kernel-based fuzzy C means (M-ARKFCM). After segmentation, statistical texture FE is connected for recognizing the patterns of cancer and non-cancer regions in mammogram images. Finally, the experimental outcome demonstrated that the proposed approach enhanced the segmentation efficiency by methods of statistical parameters contrasted with the existing operating procedures.
{"title":"Improvement of Segmentation Efficiency in Mammogram Images Using Dual-ROI Method","authors":"Venkata Satya Vivek Tammineedi, C. Raju, D. GirishKumar, Venkateswarlu Yalla","doi":"10.4018/ijhisi.305236","DOIUrl":"https://doi.org/10.4018/ijhisi.305236","url":null,"abstract":"Mammogram segmentation utilizing multi-region of intrigue is a standout amongst the most rising exploration territory in the medical image analysis. The steps engaged with the research are grouped into two kinds: 1) segmentation of mammogram images and 2) extraction of texture features from mammogram images. To overcome these difficulties, a compelling technique is proposed in this paper that comprises of three phases. In the principal arrangement, mammogram images from INbreast database are selected and improved utilizing Laplacian filtering. At that point, the pre-processed mammogram images are utilized for segmentation utilizing modified adaptively regularized kernel-based fuzzy C means (M-ARKFCM). After segmentation, statistical texture FE is connected for recognizing the patterns of cancer and non-cancer regions in mammogram images. Finally, the experimental outcome demonstrated that the proposed approach enhanced the segmentation efficiency by methods of statistical parameters contrasted with the existing operating procedures.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116268803","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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