K. Khan, M. N. Khan, Rajesh Kumar, J. Shah, Dileep Kumar, Danish Qayyum, T. Saghir, A. Shaikh, O. Shakeel, M. Karim
Aim of this study was to determine the clinical profile and angiographic pattern in young patients (≤35 years) who presented with Acute coronary syndrome (ACS) to cardiac catheterization lab of tertiary care. We prospectively recruited all young patients (≤35 years) who presented to our center with ACS from August 2020 to December 2020 and underwent coronary angiography. The primary endpoint was clinical profile including demographics, co-morbidities and angiographic findings. The secondary endpoint was in-hospital and three months mortality. A total of 1742 patients with ACS were presented to our hospital. Out of them 108 (6.2%) were ≤35 years of age. There were 86% Male, 76% fall in age group of 31–35 years. 65% were overweight. 83% were active smoker and 15% were tobacco chewer. 28% were hypertensive, 12% were diabetic and 8% were dyslipidemic. ST elevation myocardial infarction (STEMI) was the most common presentation (91%) with Anterior STEMI was the most common location (70%). Most had single vessel disease (62%) with left anterior descending (LAD) artery being the most common culprit vessel (70%). Proximal LAD was the most common site (62%) with type B lesion being the most common pattern of involvement (44%). In-hospital and at 3 months mortality was 1.9% and 4% respectively. Our findings suggest that young males were most common presenter with ACS, being overweight and smoking were the most common risk factors. Proximal LAD involvement with type B lesion causing anterior STEMI was the most common angiographic finding.
{"title":"Clinical Profile and Angiographic Pattern of Coronary Artery Disease in Young Patients with Acute Coronary Syndrome","authors":"K. Khan, M. N. Khan, Rajesh Kumar, J. Shah, Dileep Kumar, Danish Qayyum, T. Saghir, A. Shaikh, O. Shakeel, M. Karim","doi":"10.1166/jmihi.2021.3889","DOIUrl":"https://doi.org/10.1166/jmihi.2021.3889","url":null,"abstract":"Aim of this study was to determine the clinical profile and angiographic pattern in young patients (≤35 years) who presented with Acute coronary syndrome (ACS) to cardiac catheterization lab of tertiary care. We prospectively recruited all young patients (≤35 years) who presented\u0000 to our center with ACS from August 2020 to December 2020 and underwent coronary angiography. The primary endpoint was clinical profile including demographics, co-morbidities and angiographic findings. The secondary endpoint was in-hospital and three months mortality. A total of 1742 patients\u0000 with ACS were presented to our hospital. Out of them 108 (6.2%) were ≤35 years of age. There were 86% Male, 76% fall in age group of 31–35 years. 65% were overweight. 83% were active smoker and 15% were tobacco chewer. 28% were hypertensive, 12% were diabetic and 8% were dyslipidemic.\u0000 ST elevation myocardial infarction (STEMI) was the most common presentation (91%) with Anterior STEMI was the most common location (70%). Most had single vessel disease (62%) with left anterior descending (LAD) artery being the most common culprit vessel (70%). Proximal LAD was the most common\u0000 site (62%) with type B lesion being the most common pattern of involvement (44%). In-hospital and at 3 months mortality was 1.9% and 4% respectively. Our findings suggest that young males were most common presenter with ACS, being overweight and smoking were the most common risk factors. Proximal\u0000 LAD involvement with type B lesion causing anterior STEMI was the most common angiographic finding.","PeriodicalId":393031,"journal":{"name":"J. Medical Imaging Health Informatics","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131191705","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}
P. Arunachalam, P. Venkatakrishnan, N. Janakiraman, S. Sangeetha
Digital clinical histopathology is one of the crucial techniques for precise cancer cell diagnosing in modern medicine. The Synovial Sarcoma (SS) cancer cell patterns seem to be a spindle shaped cell (SSC) structure and it is very difficult to identify the exact oval shaped cell structure through pathologist’s eye perception. Meanwhile, there is necessitating for monitoring and securing the successful and effective image data processing in the the huge network data which is also a complex one. A field programmable Gate Array (FPGA) was regarded as a necessary one for this. In this work, based on FPGA a Cancer Cell classification is made for the regulation and execution. Hence, mathematically the SSC regularity structures and its discontinuities are measured by the holder exponent (HE) function. In this research work, HE values have been determined by Wavelet Transform Modulus Maxima (WTMM) and Wavelet Leader (WL) methods with basis function of Haar wavelet based on FPGA Processor. The quantitative parameters such as Mean of Asymptotic Discontinuity (MAD), Mean of Removable Discontinuity (MRD) and Number of Discontinuity Points (NDPs) have been considered to determine the prediction of discontinuity detection between WTMM and WL methods. With the help of receiver operating characteristics (ROC) curve, the significant difference of discontinuity detection performance between both the methods has been analyzed. From the experimental results, it is clear that the WL method is more practically feasible and it gives satisfactory performance, in terms of sensitivity and specificity percentage values, which are 80.56% and 59.46%, respectively in the blue color components of the SNR 20 dB noise image.
{"title":"Detection of Structure Characteristics and Its Discontinuity Based Field Programmable Gate Array Processor in Cancer Cell by Wavelet Transform","authors":"P. Arunachalam, P. Venkatakrishnan, N. Janakiraman, S. Sangeetha","doi":"10.1166/jmihi.2021.3902","DOIUrl":"https://doi.org/10.1166/jmihi.2021.3902","url":null,"abstract":"Digital clinical histopathology is one of the crucial techniques for precise cancer cell diagnosing in modern medicine. The Synovial Sarcoma (SS) cancer cell patterns seem to be a spindle shaped cell (SSC) structure and it is very difficult to identify the exact oval shaped cell\u0000 structure through pathologist’s eye perception. Meanwhile, there is necessitating for monitoring and securing the successful and effective image data processing in the the huge network data which is also a complex one. A field programmable Gate Array (FPGA) was regarded as a necessary\u0000 one for this. In this work, based on FPGA a Cancer Cell classification is made for the regulation and execution. Hence, mathematically the SSC regularity structures and its discontinuities are measured by the holder exponent (HE) function. In this research work, HE values have been\u0000 determined by Wavelet Transform Modulus Maxima (WTMM) and Wavelet Leader (WL) methods with basis function of Haar wavelet based on FPGA Processor. The quantitative parameters such as Mean of Asymptotic Discontinuity (MAD), Mean of Removable Discontinuity (MRD) and Number of Discontinuity Points\u0000 (NDPs) have been considered to determine the prediction of discontinuity detection between WTMM and WL methods. With the help of receiver operating characteristics (ROC) curve, the significant difference of discontinuity detection performance between both the methods has been analyzed. From\u0000 the experimental results, it is clear that the WL method is more practically feasible and it gives satisfactory performance, in terms of sensitivity and specificity percentage values, which are 80.56% and 59.46%, respectively in the blue color components of the SNR 20 dB noise image.","PeriodicalId":393031,"journal":{"name":"J. Medical Imaging Health Informatics","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123657478","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}
Real-time physiological data may be gathered using wearable medical sensors based on a network of body sensors. We do not however have an effective, trustworthy and secure body sensor network platform (BSN) that can satisfy growing e-health requirements. Many of these applications require BSN to provide the dependable and energy efficient data transfer of many data speeds. Cloud computing is giving assets to patient dependent on application request at SLA (service level agreement) rules. The service providers are focusing on giving the necessity based asset to satisfy the QoS (quality of service) prerequisites. Therefore, it has become an assessment to adapt service-oriented assets because of vulnerability and active interest for cloud services. The task scheduling is an option in contrast to appropriating asset by evaluating the inconsistent outstanding task at hand. the allocation of tasks given by the microprocessor Subsequently, a productive asset scheduling method needs to disseminate proper VMs (Virtual Machines). The swarm intelligence is appropriate to deal with such vulnerability issues carefully. In this paper, an effective resource scheduling strategy Utilizing Modified Particle Swarm Optimization approach (MPSO) is presented, with a target to limit execution cost that gives an approach for the microprocessor to deal with the multiple number of tasks gives to the controllers in order to perform the multiple tasks that gets logged in the cloud via Internet of things technology (Iot), energy consumed, bandwidth consumption, speed and execution cost. The near investigation of results has been exhibited that the presented scheduling scheme performed better when contrasted with existing evaluation. In this manner, the presented resource scheduling approach might be utilized to enhance the viability of cloud resources.
{"title":"Effective Resource Aware Health Care Monitoring in Body Sensor Network Platform Using Modified Particle Swarm Optimization","authors":"S. Sureshu, R. Vijayabhasker","doi":"10.1166/jmihi.2021.3895","DOIUrl":"https://doi.org/10.1166/jmihi.2021.3895","url":null,"abstract":"Real-time physiological data may be gathered using wearable medical sensors based on a network of body sensors. We do not however have an effective, trustworthy and secure body sensor network platform (BSN) that can satisfy growing e-health requirements. Many of these applications require\u0000 BSN to provide the dependable and energy efficient data transfer of many data speeds. Cloud computing is giving assets to patient dependent on application request at SLA (service level agreement) rules. The service providers are focusing on giving the necessity based asset to satisfy the QoS\u0000 (quality of service) prerequisites. Therefore, it has become an assessment to adapt service-oriented assets because of vulnerability and active interest for cloud services. The task scheduling is an option in contrast to appropriating asset by evaluating the inconsistent outstanding task at\u0000 hand. the allocation of tasks given by the microprocessor Subsequently, a productive asset scheduling method needs to disseminate proper VMs (Virtual Machines). The swarm intelligence is appropriate to deal with such vulnerability issues carefully. In this paper, an effective resource scheduling\u0000 strategy Utilizing Modified Particle Swarm Optimization approach (MPSO) is presented, with a target to limit execution cost that gives an approach for the microprocessor to deal with the multiple number of tasks gives to the controllers in order to perform the multiple tasks that gets logged\u0000 in the cloud via Internet of things technology (Iot), energy consumed, bandwidth consumption, speed and execution cost. The near investigation of results has been exhibited that the presented scheduling scheme performed better when contrasted with existing evaluation. In this manner, the presented\u0000 resource scheduling approach might be utilized to enhance the viability of cloud resources.","PeriodicalId":393031,"journal":{"name":"J. Medical Imaging Health Informatics","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114686725","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}
Automatic medical image segmentation has become increasingly important as contemporary medical imaging has become more widely available and used. Existing image segmentation solutions however lack the necessary functionality for simple medical image segmentation pipeline design. Pipelines that have already been deployed are frequently standalone software that has been optimised for a certain public data collection. As a result, the open-source python module deep-Convolutional neural network-Restricted Boltzmann Machine (deep CNNRBM) was introduced in this research work. The goal of Deep CNN-purpose RBMs is to have an easy-touse API that allows for the rapid creation of medical image segmentation transmission lines that include data augmentation, metrics, data I/O pre-processing, patch wise analysis, a library of pre-built deep neural networks, and fully automated assessment. Similarly, comprehensive pipeline customisation is possible because of strong configurability and many open interfaces. The dataset of Kidney tumor Segmentation challenge 2019 (KiTS19) acquired a strong predictor with respect to the standard 3D U-net model after cross-validation using deep CNNRBM. To that purpose, deep CNN-RBM, an expressive deep learning medical image segmentation architecture is introduced. The CNN sub-model captures frame-level spatial features automatically while the RBM submodel fuses spatial data over time to learn higher-level semantics in kidney tumor prediction. A neural network recognises medical picture segmentation, which is initiated using RBM to second-order collected data and then fine-tuned using back propagation to be more differential. According to the simulation outcome, the proposed deep CNN-RBM produced good classification results on the kidney tumour segmentation dataset.
{"title":"An Improved Kidney Tumor Prediction Using Deep Convolutional Neural Network-Restricted Boltzmann Machine Technique in Medical Image Segmentation","authors":"P. Ravikumaran, K. Devi, K. Valarmathi","doi":"10.1166/jmihi.2021.3917","DOIUrl":"https://doi.org/10.1166/jmihi.2021.3917","url":null,"abstract":"Automatic medical image segmentation has become increasingly important as contemporary medical imaging has become more widely available and used. Existing image segmentation solutions however lack the necessary functionality for simple medical image segmentation pipeline design. Pipelines\u0000 that have already been deployed are frequently standalone software that has been optimised for a certain public data collection. As a result, the open-source python module deep-Convolutional neural network-Restricted Boltzmann Machine (deep CNNRBM) was introduced in this research work. The\u0000 goal of Deep CNN-purpose RBMs is to have an easy-touse API that allows for the rapid creation of medical image segmentation transmission lines that include data augmentation, metrics, data I/O pre-processing, patch wise analysis, a library of pre-built deep neural networks, and fully automated\u0000 assessment. Similarly, comprehensive pipeline customisation is possible because of strong configurability and many open interfaces. The dataset of Kidney tumor Segmentation challenge 2019 (KiTS19) acquired a strong predictor with respect to the standard 3D U-net model after cross-validation\u0000 using deep CNNRBM. To that purpose, deep CNN-RBM, an expressive deep learning medical image segmentation architecture is introduced. The CNN sub-model captures frame-level spatial features automatically while the RBM submodel fuses spatial data over time to learn higher-level semantics in\u0000 kidney tumor prediction. A neural network recognises medical picture segmentation, which is initiated using RBM to second-order collected data and then fine-tuned using back propagation to be more differential. According to the simulation outcome, the proposed deep CNN-RBM produced good classification\u0000 results on the kidney tumour segmentation dataset.","PeriodicalId":393031,"journal":{"name":"J. Medical Imaging Health Informatics","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128922754","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}
The biomedical processing of images is an important aspect of the modern medicine field and has an immense influence on the modern world. Automatic device assisted systems are immensely useful in order to diagnose biomedical images easily, accurately and effectively. Remote health care systems allow medical professionals and patients to work from different locations. In addition, expert advice on a patient can be received within a prescribed period of time from a specialist in a foreign country or in a remote area. Digital biomedical images must be transmitted over the network in remote healthcare systems. But the delivery of the biomedical goods entails many security challenges. Patient privacy must be protected by ensuring that images are secure from unwanted access. Furthermore, it must be effectively maintained so that nothing will affect the content of biomedical images. In certain instances, data manipulation can yield dramatic effects. A biomedical image safety method was suggested in this work. The suggested method will initially be used to construct a binary pixel encoding matrix and then to adjust matrix with the use of decimation mutation DNA watermarking principle. Afterwards to defend the sub keys couple privacy which was considered over the logical uplift utilization of tent maps and purpose. As acknowledged by chaotic (C-function) development, the security was investigated similar to transmission in addition to uncertainty. Depending on the preliminary circumstances, various numbers of random were generated intended for every map as of chaotic maps. An algorithm of Multi scale grasshopper optimization resource with correlation coefficient fitness function and PSNR was projected for choosing the optimal public key and secret key of system over random numbers. For choosing the validation process of optimization is to formulate novel model more relative stable to the conventional approach. In conclusion, the considered suggested findings were contrasted with current approaches protection that was appear to be successful extremely.
{"title":"An Efficient Watermarking Based Matrix Manipulation and Optimization Based Cryptographic Method for Privacy Preservation in Biomedical Data","authors":"S. Vairaprakash, A. Shenbagavalli, S. Rajagopal","doi":"10.1166/jmihi.2021.3888","DOIUrl":"https://doi.org/10.1166/jmihi.2021.3888","url":null,"abstract":"The biomedical processing of images is an important aspect of the modern medicine field and has an immense influence on the modern world. Automatic device assisted systems are immensely useful in order to diagnose biomedical images easily, accurately and effectively. Remote health care\u0000 systems allow medical professionals and patients to work from different locations. In addition, expert advice on a patient can be received within a prescribed period of time from a specialist in a foreign country or in a remote area. Digital biomedical images must be transmitted over the network\u0000 in remote healthcare systems. But the delivery of the biomedical goods entails many security challenges. Patient privacy must be protected by ensuring that images are secure from unwanted access. Furthermore, it must be effectively maintained so that nothing will affect the content of biomedical\u0000 images. In certain instances, data manipulation can yield dramatic effects. A biomedical image safety method was suggested in this work. The suggested method will initially be used to construct a binary pixel encoding matrix and then to adjust matrix with the use of decimation mutation DNA\u0000 watermarking principle. Afterwards to defend the sub keys couple privacy which was considered over the logical uplift utilization of tent maps and purpose. As acknowledged by chaotic (C-function) development, the security was investigated similar to transmission in addition to uncertainty.\u0000 Depending on the preliminary circumstances, various numbers of random were generated intended for every map as of chaotic maps. An algorithm of Multi scale grasshopper optimization resource with correlation coefficient fitness function and PSNR was projected for choosing the optimal public\u0000 key and secret key of system over random numbers. For choosing the validation process of optimization is to formulate novel model more relative stable to the conventional approach. In conclusion, the considered suggested findings were contrasted with current approaches protection that was\u0000 appear to be successful extremely.","PeriodicalId":393031,"journal":{"name":"J. Medical Imaging Health Informatics","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127701806","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}
Rakesh Kumar Mahendran, Vishnunarayan Girishan Prabhu, P. Velusamy, A. M. Judith
Myocardial infarction (MI) may precipitate severe health damage and lead to irreversible death of the heart muscle, the result of prolonged lack of oxygen if it is not treated in a timely manner. Lack of accurate and early detection techniques for this heart disease has reduced the efficiency of MI diagnosis. In this paper, the design, and implementation of an efficient deep learning algorithm called Adaptive Recurrent neural network (ARNN) is proposed for the MI detection. The main objective of the proposed work is the accurate identification of MI disease using ECG signals. ECG signal denoising has been performed using the Multi-Notch filter, which removes the specified noise frequency range. Discrete wavelet transform (DWT) is utilized for performing the feature extraction that decomposes the ECG signal into varied scales with waveletfiltering bank. After the extraction of specific QRS features, classification of the defected and normal ECG arrhythmic beat has been performed using the deep learning-based ARNN classifier. The MIT-BIH database has been used for testing and training data. The performance of the proposed algorithm is evaluated based on classification accuracy. Results that are attained include the classification accuracy of about 99.21%, 99% of sensitivity and 99.4% of specificity with PPV and NPV of about 99.4 and 99.01 values indicate the enhanced performance of our proposed work compared with the conventional LSTM-CAE and LSTM-CNN techniques.
{"title":"Deep Learning Based Adaptive Recurrent Neural Network for Detection of Myocardial Infarction","authors":"Rakesh Kumar Mahendran, Vishnunarayan Girishan Prabhu, P. Velusamy, A. M. Judith","doi":"10.1166/jmihi.2021.3913","DOIUrl":"https://doi.org/10.1166/jmihi.2021.3913","url":null,"abstract":"Myocardial infarction (MI) may precipitate severe health damage and lead to irreversible death of the heart muscle, the result of prolonged lack of oxygen if it is not treated in a timely manner. Lack of accurate and early detection techniques for this heart disease has reduced the\u0000 efficiency of MI diagnosis. In this paper, the design, and implementation of an efficient deep learning algorithm called Adaptive Recurrent neural network (ARNN) is proposed for the MI detection. The main objective of the proposed work is the accurate identification of MI disease using ECG\u0000 signals. ECG signal denoising has been performed using the Multi-Notch filter, which removes the specified noise frequency range. Discrete wavelet transform (DWT) is utilized for performing the feature extraction that decomposes the ECG signal into varied scales with waveletfiltering bank.\u0000 After the extraction of specific QRS features, classification of the defected and normal ECG arrhythmic beat has been performed using the deep learning-based ARNN classifier. The MIT-BIH database has been used for testing and training data. The performance of the proposed algorithm is evaluated\u0000 based on classification accuracy. Results that are attained include the classification accuracy of about 99.21%, 99% of sensitivity and 99.4% of specificity with PPV and NPV of about 99.4 and 99.01 values indicate the enhanced performance of our proposed work compared with the conventional\u0000 LSTM-CAE and LSTM-CNN techniques.","PeriodicalId":393031,"journal":{"name":"J. Medical Imaging Health Informatics","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128632152","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}
Data sharing is essential in present biomedical research. A large quantity of medical information is gathered and for different objectives of analysis and study. Because of its large collection, anonymity is essential. Thus, it is quite important to preserve privacy and prevent leakage of sensitive information of patients. Most of the Anonymization methods such as generalisation, suppression and perturbation are proposed to overcome the information leak which degrades the utility of the collected data. During data sanitization, the utility is automatically diminished. Privacy Preserving Data Publishing faces the main drawback of maintaining tradeoff between privacy and data utility. To address this issue, an efficient algorithm called Anonymization based on Improved Bucketization (AIB) is proposed, which increases the utility of published data while maintaining privacy. The Bucketization technique is used in this paper with the intervention of the clustering method. The proposed work is divided into three stages: (i) Vertical and Horizontal partitioning (ii) Assigning Sensitive index to attributes in the cluster (iii) Verifying each cluster against privacy threshold (iv) Examining for privacy breach in Quasi Identifier (QI). To increase the utility of published data, the threshold value is determined based on the distribution of elements in each attribute, and the anonymization method is applied only to the specific QI element. As a result, the data utility has been improved. Finally, the evaluation results validated the design of paper and demonstrated that our design is effective in improving data utility.
{"title":"Anonymization Based on Improved Bucketization (AIB): A Privacy-Preserving Data Publishing Technique for Improving Data Utility in Healthcare Data","authors":"R. Indhumathi, S. Devi","doi":"10.1166/jmihi.2021.3901","DOIUrl":"https://doi.org/10.1166/jmihi.2021.3901","url":null,"abstract":"Data sharing is essential in present biomedical research. A large quantity of medical information is gathered and for different objectives of analysis and study. Because of its large collection, anonymity is essential. Thus, it is quite important to preserve privacy and prevent leakage\u0000 of sensitive information of patients. Most of the Anonymization methods such as generalisation, suppression and perturbation are proposed to overcome the information leak which degrades the utility of the collected data. During data sanitization, the utility is automatically diminished. Privacy\u0000 Preserving Data Publishing faces the main drawback of maintaining tradeoff between privacy and data utility. To address this issue, an efficient algorithm called Anonymization based on Improved Bucketization (AIB) is proposed, which increases the utility of published data while maintaining\u0000 privacy. The Bucketization technique is used in this paper with the intervention of the clustering method. The proposed work is divided into three stages: (i) Vertical and Horizontal partitioning (ii) Assigning Sensitive index to attributes in the cluster (iii) Verifying each cluster against\u0000 privacy threshold (iv) Examining for privacy breach in Quasi Identifier (QI). To increase the utility of published data, the threshold value is determined based on the distribution of elements in each attribute, and the anonymization method is applied only to the specific QI element. As a\u0000 result, the data utility has been improved. Finally, the evaluation results validated the design of paper and demonstrated that our design is effective in improving data utility.","PeriodicalId":393031,"journal":{"name":"J. Medical Imaging Health Informatics","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126485218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Image segmentation plays a very vital role in gathering information by dividing the images into various segments to achieve the meaningful information, whereas the image segmentation gives importance in the area of medical imaging to analyze and process the anatomical structures of various internal organs of the body with high resolution images that are captured during medical examination. Medical experts will go through the reports which give the various reasons for the existence of the disease. Brain which is considered the important part of the body so the detection and the segmentation of brain tumors will be considered as the major task of the medical field whereas they are using the high resolution images in the form of MRI reports. The MRI images are considered as the vital source for the identification of tumors in the brain. The accuracy of the segmentation and identification of the tumor depends upon the experience of the radiologist and also it is time consuming task. Therefore the watershed segmentation is performed for the extraction of the tumor region and the features are extracted for the classification, whereas the classification is carried out by the Feed-Forward Neural Network (FNN). The experimental results are evaluated based on the performance and the quality analysis, Furthermore the results give the accuracy of 91.2% in the training model and 71.8% as the testing during the classification process.
{"title":"Brain Tumor Detection with Biologically Inspired Watershed Segmentation and Classification Based on Feed-Forward Neural Network (FNN)","authors":"G. Gopika, J. Shanthini, M. Kavitha, R. Sabitha","doi":"10.1166/jmihi.2021.3909","DOIUrl":"https://doi.org/10.1166/jmihi.2021.3909","url":null,"abstract":"Image segmentation plays a very vital role in gathering information by dividing the images into various segments to achieve the meaningful information, whereas the image segmentation gives importance in the area of medical imaging to analyze and process the anatomical structures of\u0000 various internal organs of the body with high resolution images that are captured during medical examination. Medical experts will go through the reports which give the various reasons for the existence of the disease. Brain which is considered the important part of the body so the detection\u0000 and the segmentation of brain tumors will be considered as the major task of the medical field whereas they are using the high resolution images in the form of MRI reports. The MRI images are considered as the vital source for the identification of tumors in the brain. The accuracy of the\u0000 segmentation and identification of the tumor depends upon the experience of the radiologist and also it is time consuming task. Therefore the watershed segmentation is performed for the extraction of the tumor region and the features are extracted for the classification, whereas the classification\u0000 is carried out by the Feed-Forward Neural Network (FNN). The experimental results are evaluated based on the performance and the quality analysis, Furthermore the results give the accuracy of 91.2% in the training model and 71.8% as the testing during the classification process.","PeriodicalId":393031,"journal":{"name":"J. Medical Imaging Health Informatics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129365508","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}
Vehicle Ad Hoc Networks (VANETs) is a crucial communications framework for transferring messages between any healthcare systems. The dilemma of fixing the safest efficient route is a tedious issue in VANET. Hence the secure and most reliable way will give the appropriate solution for the routing issues in the VANET. In this paper, by using the Multi-Objective Bio-inspired Heuristic Cuckoo Search Node optimization algorithm is designed to find the efficient safest route for transferring health data within a short period. After seeing the efficient route, the node can be distinguished upon the traffic and security by using the Stochastic Discriminant Random Forest Node Classifier. Then in the selected route, the nodal distance can be calculated by applying the delay-based weighted end-to-end approach for traffic analysis. Then the authentic vehicle node can be analyzed through the Trust Aware extreme Gradient Boosting Node Classification based Secured Routing (TAXGBNC-SR) Technique. The obtained information that can be stored in the cloud. It deal with the multiple number of tasks gives to the ARM micro-controllers in order to perform the multiple tasks that gets logged in the cloud via Internet of Things technology (Iot).
{"title":"Efficient and Secure Remote Health Management in Cloud in Vehicular Adhoc Network Environment","authors":"K. Mohanaprakash, T. Gunasekar","doi":"10.1166/jmihi.2021.3905","DOIUrl":"https://doi.org/10.1166/jmihi.2021.3905","url":null,"abstract":"Vehicle Ad Hoc Networks (VANETs) is a crucial communications framework for transferring messages between any healthcare systems. The dilemma of fixing the safest efficient route is a tedious issue in VANET. Hence the secure and most reliable way will give the appropriate solution for\u0000 the routing issues in the VANET. In this paper, by using the Multi-Objective Bio-inspired Heuristic Cuckoo Search Node optimization algorithm is designed to find the efficient safest route for transferring health data within a short period. After seeing the efficient route, the node can be\u0000 distinguished upon the traffic and security by using the Stochastic Discriminant Random Forest Node Classifier. Then in the selected route, the nodal distance can be calculated by applying the delay-based weighted end-to-end approach for traffic analysis. Then the authentic vehicle node can\u0000 be analyzed through the Trust Aware extreme Gradient Boosting Node Classification based Secured Routing (TAXGBNC-SR) Technique. The obtained information that can be stored in the cloud. It deal with the multiple number of tasks gives to the ARM micro-controllers in order to perform the multiple\u0000 tasks that gets logged in the cloud via Internet of Things technology (Iot).","PeriodicalId":393031,"journal":{"name":"J. Medical Imaging Health Informatics","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133320806","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}
At present, heart disease not only has a significant impact on the quality of human life but also poses a greater impact on people’s health. Therefore, it is very important to be able to diagnose heart disease as early as possible and give corresponding treatment. Heart image segmentation is the primary operation of intelligent heart disease diagnosis. The quality of segmentation directly determines the effect of intelligent diagnosis. Because the running time of image segmentation is often longer, coupled with the characteristics of cardiac MR imaging technology and the structural characteristics of the cardiac target itself, the rapid segmentation of cardiac MRI images still has challenges. Aiming at the long running time of traditional methods and low segmentation accuracy, a medical image segmentation (MIS) method based on particle swarm optimization (PSO) optimized support vector machine (SVM) is proposed, referred to as PSO-SVM. First, the current iteration number and population number in PSO are added to the control strategy of inertial weight λ to improve the performance of PSO inertial weight λ. Find the optimal penalty coefficient C and γ in the gaussian kernel function by PSO. Then use the SVM method to establish the best classification model and test the data. Compared with traditional methods, this method not only shortens the running time, but also improves the segmentation accuracy. At the same time, comparing the influence of traditional inertial weights on segmentation results, the improved method reduces the average convergence algebra and shortens the optimization time.
{"title":"A Novel Image Segmentation Method for Cardiac MRI Using Support Vector Machine Algorithm Based on Particle Swarm Optimization","authors":"Guanghui Wang, Lihong Ma","doi":"10.1166/jmihi.2021.3510","DOIUrl":"https://doi.org/10.1166/jmihi.2021.3510","url":null,"abstract":"At present, heart disease not only has a significant impact on the quality of human life but also poses a greater impact on people’s health. Therefore, it is very important to be able to diagnose heart disease as early as possible and give corresponding treatment. Heart image\u0000 segmentation is the primary operation of intelligent heart disease diagnosis. The quality of segmentation directly determines the effect of intelligent diagnosis. Because the running time of image segmentation is often longer, coupled with the characteristics of cardiac MR imaging technology\u0000 and the structural characteristics of the cardiac target itself, the rapid segmentation of cardiac MRI images still has challenges. Aiming at the long running time of traditional methods and low segmentation accuracy, a medical image segmentation (MIS) method based on particle swarm optimization\u0000 (PSO) optimized support vector machine (SVM) is proposed, referred to as PSO-SVM. First, the current iteration number and population number in PSO are added to the control strategy of inertial weight λ to improve the performance of PSO inertial weight λ. Find the\u0000 optimal penalty coefficient C and γ in the gaussian kernel function by PSO. Then use the SVM method to establish the best classification model and test the data. Compared with traditional methods, this method not only shortens the running time, but also improves the segmentation\u0000 accuracy. At the same time, comparing the influence of traditional inertial weights on segmentation results, the improved method reduces the average convergence algebra and shortens the optimization time.","PeriodicalId":393031,"journal":{"name":"J. Medical Imaging Health Informatics","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132527072","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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