The association rule mining approach produces uninteresting association rules. When the set of association rules become large, it becomes less interesting to the user. In order to pick interesting association rules among peak volumes of found association rules, it is critical to aid the decision-maker with an efficient post-processing phase. Theymotivate the need for association analysis performance. Practically it is an overhead to analyze the large set of association rules. In this work, association rule pruning technique called Class Based Association Rule Pruning (CBARP). This pruning techniques is proposed to prune the weak association rules of the healthcare system. The results are compared with Semantic Tree Based Association Rule Mining (STAR) technique and it demonstrate that the CBARP method outperforms other methods for the given support values.
{"title":"Application of Class Based Association Rule Pruning to Generate Optimal Association Rules in Healthcare","authors":"D. Sasikala, K. Premalatha","doi":"10.1166/jmihi.2021.3876","DOIUrl":"https://doi.org/10.1166/jmihi.2021.3876","url":null,"abstract":"The association rule mining approach produces uninteresting association rules. When the set of association rules become large, it becomes less interesting to the user. In order to pick interesting association rules among peak volumes of found association rules, it is critical to aid\u0000 the decision-maker with an efficient post-processing phase. Theymotivate the need for association analysis performance. Practically it is an overhead to analyze the large set of association rules. In this work, association rule pruning technique called Class Based Association Rule Pruning\u0000 (CBARP). This pruning techniques is proposed to prune the weak association rules of the healthcare system. The results are compared with Semantic Tree Based Association Rule Mining (STAR) technique and it demonstrate that the CBARP method outperforms other methods for the given support values.","PeriodicalId":393031,"journal":{"name":"J. Medical Imaging Health Informatics","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128348666","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}
Mohammad Alarifi, Timothy Patrick, A. Jabour, Min Wu, Jake Luo
Patient literacy of radiology is imperative for patient engagement in care and management of their own health. Little is known about the factors that could predict patient literacy of radiology reports, testing, or treatment. This study aims to identify the most important factors of health consumer social economic and health conditions as a predictor of health literacy in the radiology domain. The study recruited 616 participants using Amazon.com’s Mechanical Turk (MTURK) and presented these participants with our questionnaire. We measured the level of participants’ radiology awareness, social factors, and health status. Descriptive statics including Chi-Square and linear regression models were used to test if the factors could predict radiology literacy. The area under the receiver–operator curve was calculated to determine the prediction accuracy of the regression models. linear regression indicated that 15 of the 19 social-economic factors and health conditions were significantly associated with radiology literacy (P < .05). On the other hand, only 12 of the 19 factors were significant by using Pearson Chi-Square (P < .05). Stepwise linear regression analysis demonstrated the r squared linear of 9 out of 12 common factors. These factors are the level of education, smoking, radiology experience, insurance status, white race, employment status, disability status, gender, and income at 0.209. These nine factors had a good ability to predict radiology literacy (area under the receiver operator curve of 0.677 [95%CI 0.549; 0.804, P = 0.013]). Social economic factors and health conditions can be used to successfully predict radiology literacy. We were able to successfully identify the predictive factors that have a high association with the radiology literacy by comparing social factors and health status versus radiology awareness.
患者对放射学的了解是患者参与护理和管理自己健康的必要条件。很少知道的因素，可以预测病人的识字放射学报告，测试，或治疗。本研究旨在确定健康消费、社会经济和健康状况的最重要因素，作为放射学领域健康素养的预测因子。这项研究招募了616名参与者，使用亚马逊的土耳其机器人(MTURK)，并向这些参与者发放了我们的问卷。我们测量了参与者的放射学认知水平、社会因素和健康状况。描述性统计包括卡方和线性回归模型来检验这些因素是否可以预测放射学素养。计算接受者-操作者曲线下面积，确定回归模型的预测精度。线性回归显示，19个社会经济因素和健康状况中有15个因素与放射学素养显著相关(P < 0.05)。另一方面，使用皮尔逊卡方分析，19个因素中只有12个具有显著性(P < 0.05)。逐步线性回归分析表明，12个公因式因子中有9个的r平方呈线性关系。这些因素是教育水平、吸烟、放射经验、保险状况、白人种族、就业状况、残疾状况、性别和收入(0.209)。这9个因素具有较好的预测放射学素养的能力(受试者操作曲线下面积为0.677 [95%CI为0.549;0.804, p = 0.013])。社会经济因素和健康状况可以成功地预测放射学素养。通过比较社会因素、健康状况与放射学意识，我们能够成功地识别出与放射学素养高度相关的预测因素。
{"title":"Health Consumer Social Economic Factors and Health Conditions as Predictor for Health Literacy in Radiology Domain","authors":"Mohammad Alarifi, Timothy Patrick, A. Jabour, Min Wu, Jake Luo","doi":"10.1166/jmihi.2021.3864","DOIUrl":"https://doi.org/10.1166/jmihi.2021.3864","url":null,"abstract":"Patient literacy of radiology is imperative for patient engagement in care and management of their own health. Little is known about the factors that could predict patient literacy of radiology reports, testing, or treatment. This study aims to identify the most important factors of\u0000 health consumer social economic and health conditions as a predictor of health literacy in the radiology domain. The study recruited 616 participants using Amazon.com’s Mechanical Turk (MTURK) and presented\u0000 these participants with our questionnaire. We measured the level of participants’ radiology awareness, social factors, and health status. Descriptive statics including Chi-Square and linear regression models were used to test if the factors could predict radiology literacy. The area\u0000 under the receiver–operator curve was calculated to determine the prediction accuracy of the regression models. linear regression indicated that 15 of the 19 social-economic factors and health conditions were significantly associated with radiology literacy (P < .05). On the\u0000 other hand, only 12 of the 19 factors were significant by using Pearson Chi-Square (P < .05). Stepwise linear regression analysis demonstrated the r squared linear of 9 out of 12 common factors. These factors are the level of education, smoking, radiology experience, insurance status,\u0000 white race, employment status, disability status, gender, and income at 0.209. These nine factors had a good ability to predict radiology literacy (area under the receiver operator curve of 0.677 [95%CI 0.549; 0.804, P = 0.013]). Social economic factors and health conditions can be\u0000 used to successfully predict radiology literacy. We were able to successfully identify the predictive factors that have a high association with the radiology literacy by comparing social factors and health status versus radiology awareness.","PeriodicalId":393031,"journal":{"name":"J. Medical Imaging Health Informatics","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127192990","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}
Zhan Wu, Rongjun Ge, Y. Chen, Xiaopu He, L. Luo, Yu Cao, Hengyong Yu
The outbreak of 2019 novel coronavirus (COVID-19) has caused more than 176 million confirmed cases by June 14, 2021, and this number will continue to grow. Automatic and accurate COVID-19 detection/evaluation from the computed tomography (CT) scans is of great significance for COVID-19 diagnosis and treatment. Due to individual variations of patients and the influx of a large number of patients, the current clinical practices remain subject to shortcomings of potential high-risk and time-consumption issues from radiologists. In this paper, we propose a computer aided detection system to relieve the clinical physicians from tediously reading the CT images of COVID-19 patients. Particularly, a COVID-19 detection network (COVIDNet) is proposed using deep convolutional neural networks (DCNNs) for patient-level COVID-19 detection to distinguish infected and non-infected patients. The underlying method complementarily and comprehensively extract multi-level interplane volumetric correlation features of typical ground glass opacities (GGOs) lesions using 3D multi-Scale Network (MSN). To cover more GGO lesion features and reduce intra-class differences, a Phase Ensemble (PE) is proposed for aggregation of different phases in one CT scan. The proposed method is evaluated on a clinically established COVID-19 database with five-fold cross-validation. Experimental results show that the proposed framework achieves classification performance with the specificity of 1.0000, sensitivity of 0.9700, accuracy of 0.9850, precision of 1.0000, and Area Under the Curve (AUC) of 0.9980. All of these indicate that our method enables an efficient, accurate and reliable patient-level COVID-19 detection for clinical diagnosis. This can significantly improve the work efficiency of clinical physicians on COVID-19 patient diagnosis and evaluation in hospitals and clinics. Impact statement—The proposed method can automatically and accurately distinguish the COVID-19 patients from patient-level CT scan images. On a clinically established large-scale COVID-19 database with five-fold cross-validation, the experimental results show that the proposed framework achieves a classification performance with the specificity of 1.0000, sensitivity of 0.9700, accuracy of 0.9850, precision of 1.0000, and Area Under the Curve (AUC) of 0.9980. It can relieve the clinical physicians from tediously reading the CT images of COVID-19 patients. Thus, it can significantly improve the work efficiency of clinical physicians on COVID-19 patient diagnosis and evaluation in hospitals and clinics, particularly in the pandemic period of COVID-19.
{"title":"Automatic Patient-Level Detection of Coronavirus Disease (COVID-19) Using Convolutional Neural Network from Lung CT Scans","authors":"Zhan Wu, Rongjun Ge, Y. Chen, Xiaopu He, L. Luo, Yu Cao, Hengyong Yu","doi":"10.1166/jmihi.2021.3865","DOIUrl":"https://doi.org/10.1166/jmihi.2021.3865","url":null,"abstract":"The outbreak of 2019 novel coronavirus (COVID-19) has caused more than 176 million confirmed cases by June 14, 2021, and this number will continue to grow. Automatic and accurate COVID-19 detection/evaluation from the computed tomography (CT) scans is of great significance for COVID-19\u0000 diagnosis and treatment. Due to individual variations of patients and the influx of a large number of patients, the current clinical practices remain subject to shortcomings of potential high-risk and time-consumption issues from radiologists. In this paper, we propose a computer aided detection\u0000 system to relieve the clinical physicians from tediously reading the CT images of COVID-19 patients. Particularly, a COVID-19 detection network (COVIDNet) is proposed using deep convolutional neural networks (DCNNs) for patient-level COVID-19 detection to distinguish infected and non-infected\u0000 patients. The underlying method complementarily and comprehensively extract multi-level interplane volumetric correlation features of typical ground glass opacities (GGOs) lesions using 3D multi-Scale Network (MSN). To cover more GGO lesion features and reduce intra-class differences, a Phase\u0000 Ensemble (PE) is proposed for aggregation of different phases in one CT scan. The proposed method is evaluated on a clinically established COVID-19 database with five-fold cross-validation. Experimental results show that the proposed framework achieves classification performance with the specificity\u0000 of 1.0000, sensitivity of 0.9700, accuracy of 0.9850, precision of 1.0000, and Area Under the Curve (AUC) of 0.9980. All of these indicate that our method enables an efficient, accurate and reliable patient-level COVID-19 detection for clinical diagnosis. This can significantly improve the\u0000 work efficiency of clinical physicians on COVID-19 patient diagnosis and evaluation in hospitals and clinics. Impact statement—The proposed method can automatically and accurately distinguish the COVID-19 patients from patient-level CT scan images. On a clinically established\u0000 large-scale COVID-19 database with five-fold cross-validation, the experimental results show that the proposed framework achieves a classification performance with the specificity of 1.0000, sensitivity of 0.9700, accuracy of 0.9850, precision of 1.0000, and Area Under the Curve (AUC) of 0.9980.\u0000 It can relieve the clinical physicians from tediously reading the CT images of COVID-19 patients. Thus, it can significantly improve the work efficiency of clinical physicians on COVID-19 patient diagnosis and evaluation in hospitals and clinics, particularly in the pandemic period of COVID-19.","PeriodicalId":393031,"journal":{"name":"J. Medical Imaging Health Informatics","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131858846","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}
Elderly persons are generally prone to CHDs (Chronic Heart Diseases). Arrhythmia is a persistent CHD with high mortalities resulting from cardiac failures, heart strokes, and CADs (Coronary Artery Diseases). Arrhythmia can be detected using ECG (Electrocardiogram) signals. ECG signals need to be pre-processed for removing noises present in signals. Since denoising is a significant step in ECG signals. Recently Support Vector Machine -Radial Bias Function (SVM-RBF) classifier is introduced for arrhythmia classification, it doesn’t remove noises presented from the ECG signals. The major aim of the work is to design a new classifier with removed noises and enhanced ECG signal. In this work, EMDs (Empirical Mode Decompositions) is introduced for noise removing which works recursively and dependent on signals called sifting. In EMD, IMFs (Intrinsic Mode Functions) decompose noisy signals into intrinsic oscillatory components adaptively using sifting. Further, FWBSOs (Fuzzy Weight Beetle Swarm Optimizations) are used in this work for optimizing EMDs and IMFs. This work in the initial phase reconstructs ECG signals which are filtered by IMFs. These filters are followed by extraction of morphological features from waves of P-QRS-T while ECG segments are selected using PCAs and DTWs. In the final phase, EKSVMs (Enhanced Kernel Support Vector Machines) classifies extracted features automatically by categorizing ECG signals into Normal and Ventricular Ectopic Beats. This work’s resulted are evaluated with performance metrics of Sensitivity, F-measure, Positive Productivity and Accuracy. This work uses database of MIT-BIH arrhythmia in a 5 fold cross validation for its predictions. The proposed EKSVMs classifier is compared to existing classifiers such as K-Nearest Neighbors (KNN), Enhanced Particle Swarm Optimisation-Multiple Layer Perception (EPSO-MLP) and SVM-RBF. The experiments of the proposed classifier and existing methods are carried out on MATLAB R2018a.
老年人一般容易患冠心病(慢性心脏病)。心律失常是一种持续性冠心病，由心力衰竭、中风和冠心病引起，死亡率高。心律失常可以通过心电图信号检测出来。心电信号需要进行预处理以去除信号中存在的噪声。因为去噪是心电信号处理的重要步骤。近年来引入支持向量机-径向偏置函数(SVM-RBF)分类器进行心律失常分类，该分类器不去除心电信号中的噪声。本工作的主要目的是设计一种新的去噪和增强心电信号的分类器。在这项工作中，引入了emd(经验模式分解)来去除递归工作并依赖于称为筛选的信号。在EMD中，IMFs(本征模态函数)通过筛选自适应地将噪声信号分解为本征振荡分量。此外，fwbso(模糊权重甲虫群优化)在这项工作中用于优化emd和imf。该工作在初始阶段重建经imf滤波后的心电信号。这些滤波器之后，从P-QRS-T波中提取形态特征，同时使用pca和DTWs选择ECG段。在最后阶段，增强核支持向量机(Enhanced Kernel Support Vector Machines, eksvm)通过将心电信号自动分类为正常和室性异位搏，对提取的特征进行自动分类。用灵敏度、f值、正生产率和准确性等绩效指标对工作结果进行了评价。本研究使用MIT-BIH心律失常数据库进行5倍交叉验证。将提出的eksvm分类器与k -最近邻(KNN)、增强粒子群优化-多层感知(EPSO-MLP)和SVM-RBF等现有分类器进行了比较。在MATLAB R2018a上对所提出的分类器和现有方法进行了实验。
{"title":"Empirical Mode Decomposition with Fuzzy Weight Beetle Swarm Optimization (EMD-FWBSO) Denoising and Enhanced Kernel Support Vector Machine (EKSVM) Classifier for Arrhythmia in Electrocardiogram Recordings","authors":"R. R. Thirrunavukkarasu, T. Devi","doi":"10.1166/jmihi.2021.3870","DOIUrl":"https://doi.org/10.1166/jmihi.2021.3870","url":null,"abstract":"Elderly persons are generally prone to CHDs (Chronic Heart Diseases). Arrhythmia is a persistent CHD with high mortalities resulting from cardiac failures, heart strokes, and CADs (Coronary Artery Diseases). Arrhythmia can be detected using ECG (Electrocardiogram) signals. ECG signals\u0000 need to be pre-processed for removing noises present in signals. Since denoising is a significant step in ECG signals. Recently Support Vector Machine -Radial Bias Function (SVM-RBF) classifier is introduced for arrhythmia classification, it doesn’t remove noises presented from the ECG\u0000 signals. The major aim of the work is to design a new classifier with removed noises and enhanced ECG signal. In this work, EMDs (Empirical Mode Decompositions) is introduced for noise removing which works recursively and dependent on signals called sifting. In EMD, IMFs (Intrinsic Mode Functions)\u0000 decompose noisy signals into intrinsic oscillatory components adaptively using sifting. Further, FWBSOs (Fuzzy Weight Beetle Swarm Optimizations) are used in this work for optimizing EMDs and IMFs. This work in the initial phase reconstructs ECG signals which are filtered by IMFs. These filters\u0000 are followed by extraction of morphological features from waves of P-QRS-T while ECG segments are selected using PCAs and DTWs. In the final phase, EKSVMs (Enhanced Kernel Support Vector Machines) classifies extracted features automatically by categorizing ECG signals into Normal and Ventricular\u0000 Ectopic Beats. This work’s resulted are evaluated with performance metrics of Sensitivity, F-measure, Positive Productivity and Accuracy. This work uses database of MIT-BIH arrhythmia in a 5 fold cross validation for its predictions. The proposed EKSVMs classifier is compared to existing\u0000 classifiers such as K-Nearest Neighbors (KNN), Enhanced Particle Swarm Optimisation-Multiple Layer Perception (EPSO-MLP) and SVM-RBF. The experiments of the proposed classifier and existing methods are carried out on MATLAB R2018a.","PeriodicalId":393031,"journal":{"name":"J. Medical Imaging Health Informatics","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127711300","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}
Skin cancer is considered one of the most common type of cancer in several countries. Due to the difficulty and subjectivity in the clinical diagnosis of skin lesions, Computer-Aided Diagnosis systems are being developed for assist experts to perform more reliable diagnosis. The clinical analysis and diagnosis of skin lesions relies not only on the visual information but also on the context information provided by the patient. Skin lesion segmentation plays a significant part in the earlier and precise identification of skin cancer using computer aided diagnosis (CAD) models. But, the segmentation of skin lesions in dermoscopic images is a difficult process due to the constraints of artefacts (hairs, gel bubbles, ruler markers), unclear boundaries, poor and so on. In this work, multi class skin lesion classification system is developed based on multi layered Fuzzy C-means clustering and deep convolutional neural networks. Evaluate the performance of the proposed MLFCM with DCNN model on multi class skin cancer Dermoscopy images. Our results suggest that it is possible to boost the performance of skin lesion segmentation and classification simultaneously via training a unified model to perform both tasks in a mutual bootstrapping way.
{"title":"Hybrid Melanoma Classification System Using Multi-Layer Fuzzy C-Means Clustering and Deep Convolutional Neural Network","authors":"A. Jayachandran, B. AnuSheeba","doi":"10.1166/jmihi.2021.3873","DOIUrl":"https://doi.org/10.1166/jmihi.2021.3873","url":null,"abstract":"Skin cancer is considered one of the most common type of cancer in several countries. Due to the difficulty and subjectivity in the clinical diagnosis of skin lesions, Computer-Aided Diagnosis systems are being developed for assist experts to perform more reliable diagnosis. The clinical\u0000 analysis and diagnosis of skin lesions relies not only on the visual information but also on the context information provided by the patient. Skin lesion segmentation plays a significant part in the earlier and precise identification of skin cancer using computer aided diagnosis (CAD) models.\u0000 But, the segmentation of skin lesions in dermoscopic images is a difficult process due to the constraints of artefacts (hairs, gel bubbles, ruler markers), unclear boundaries, poor and so on. In this work, multi class skin lesion classification system is developed based on multi layered Fuzzy\u0000 C-means clustering and deep convolutional neural networks. Evaluate the performance of the proposed MLFCM with DCNN model on multi class skin cancer Dermoscopy images. Our results suggest that it is possible to boost the performance of skin lesion segmentation and classification simultaneously\u0000 via training a unified model to perform both tasks in a mutual bootstrapping way.","PeriodicalId":393031,"journal":{"name":"J. Medical Imaging Health Informatics","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128826901","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}
A cognitive wireless powered communication network (CWPCN) for spectrum distribution in IoMT based healthcare systems is employed with a principal network, which in turn deals with security issues from various attacks like Denial of Service (DoS), Man-In-the-Middle, or phishing attacks. In this, a new protocol is proposed for wireless powered SU (secondary users) so as to cooperate with PU (primary user) of the healthcare network. At the time of wireless power transfer (WPT) in a IoMT based healthcare network, the first harvest energy of SUs was carried from power signals broadcasted by the cognitive hybrid access point. Then the harvested energy is employed while gaining transmission opportunities simultaneously all through the phase of Wireless Information Transfer (WIT) of healthcare system. Furthermore, Fuzzy based cluster greedy algorithm is introduced for reducing the interruption of PU secrecy prospect and to offer the best optimal values in the healthcare data. In this approach, the injection impact and reactive jamming attacks on wireless transmission are analyzed. These can be recognized through a Convolutional Neural Network (CNN) to detect different attack types and classify them. Finally, the results were compared with the existing method.
{"title":"Fuzzy Based Cluster Greedy Optimization and Convolutional Neural Networks Based Scheme for Internet of Medical Things Based Healthcare Resource Allocation in Cognitive Wireless Powered Communication Network","authors":"M. Bhuvaneswari, S. Sasipriya","doi":"10.1166/jmihi.2021.3863","DOIUrl":"https://doi.org/10.1166/jmihi.2021.3863","url":null,"abstract":"A cognitive wireless powered communication network (CWPCN) for spectrum distribution in IoMT based healthcare systems is employed with a principal network, which in turn deals with security issues from various attacks like Denial of Service (DoS), Man-In-the-Middle, or phishing attacks.\u0000 In this, a new protocol is proposed for wireless powered SU (secondary users) so as to cooperate with PU (primary user) of the healthcare network. At the time of wireless power transfer (WPT) in a IoMT based healthcare network, the first harvest energy of SUs was carried from power signals\u0000 broadcasted by the cognitive hybrid access point. Then the harvested energy is employed while gaining transmission opportunities simultaneously all through the phase of Wireless Information Transfer (WIT) of healthcare system. Furthermore, Fuzzy based cluster greedy algorithm is introduced\u0000 for reducing the interruption of PU secrecy prospect and to offer the best optimal values in the healthcare data. In this approach, the injection impact and reactive jamming attacks on wireless transmission are analyzed. These can be recognized through a Convolutional Neural Network (CNN)\u0000 to detect different attack types and classify them. Finally, the results were compared with the existing method.","PeriodicalId":393031,"journal":{"name":"J. Medical Imaging Health Informatics","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125573326","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 recent years, a usual worldwide problem is a skin disease—the diagnostics of infection and skin disease prediction based on the data mining techniques. The precise and cost-effective treatments obtain a technologybased data mining system that can consider making the right decision. Depends on data, there are 34 UCI datasets have in the skin disease prediction. All of the datasets are not much important when predicting the skin disease problem. In this study, the essential datasets to be analyzed because they only give the best accuracy in skin disease prediction. For an outstanding selection of allocation, to propose a novel feature selection approaches, Enriched Fruitfly Optimization Algorithm (EFOA), and Ensemble Classifiers that are helps for an early stage of skin disease prediction. A hybrid technique through the three essential hybrid feature selection approaches such as Chi-Square method, Information Gain method, and Principal Component Analysis (PCA) methods that are combined for better feature selection results. Based on the skin disease dataset, the resultant feature selection approach generated the reduced data subset. Then, the Enriched Fruitfly Optimization Algorithm (EFOA) offers the optimization of reduced data subset. Here, the accuracy estimation is the vital factor to optimize the effective and best prediction of skin disease affected regions. Afterward, the classification performs to classify the EFOA based optimized result by using the six different classification methods. Where, the classification helps to analyze the optimized results, which offers the better classification procedure. To predict the base learner’s performance, to utilize the Naive Bayesian, K-Nearest Neighbour, Decision Tree, Support Vector Machine, Random Forest, and Multilayer Perceptron (MLP) to classify the optimized result. Then, the ensemble techniques used to analyze the classifier’s results through the 3 different methods like Bagging, Boosting, Stacking, added on the base learners to improve the proposed work. Based on the performance, the base learners’ performance is larger than the input dataset. The base learner’s parameters are essential to calculate the accuracy of skin disease prediction performance. The performance of the proposed method will take and compare to each base learner, and the performance shows the accurate skin disease prediction improvement with other existing methods.
{"title":"Enriched Optimization Algorithm for Effective Skin Disease Prediction Using Soft Computing Techniques","authors":"R. S. Kumar, R. Dhanagopal, S. S. Kumar","doi":"10.1166/jmihi.2021.3882","DOIUrl":"https://doi.org/10.1166/jmihi.2021.3882","url":null,"abstract":"In recent years, a usual worldwide problem is a skin disease—the diagnostics of infection and skin disease prediction based on the data mining techniques. The precise and cost-effective treatments obtain a technologybased data mining system that can consider making the right decision.\u0000 Depends on data, there are 34 UCI datasets have in the skin disease prediction. All of the datasets are not much important when predicting the skin disease problem. In this study, the essential datasets to be analyzed because they only give the best accuracy in skin disease prediction. For\u0000 an outstanding selection of allocation, to propose a novel feature selection approaches, Enriched Fruitfly Optimization Algorithm (EFOA), and Ensemble Classifiers that are helps for an early stage of skin disease prediction. A hybrid technique through the three essential hybrid feature selection\u0000 approaches such as Chi-Square method, Information Gain method, and Principal Component Analysis (PCA) methods that are combined for better feature selection results. Based on the skin disease dataset, the resultant feature selection approach generated the reduced data subset. Then, the Enriched\u0000 Fruitfly Optimization Algorithm (EFOA) offers the optimization of reduced data subset. Here, the accuracy estimation is the vital factor to optimize the effective and best prediction of skin disease affected regions. Afterward, the classification performs to classify the EFOA based optimized\u0000 result by using the six different classification methods. Where, the classification helps to analyze the optimized results, which offers the better classification procedure. To predict the base learner’s performance, to utilize the Naive Bayesian, K-Nearest Neighbour, Decision Tree,\u0000 Support Vector Machine, Random Forest, and Multilayer Perceptron (MLP) to classify the optimized result. Then, the ensemble techniques used to analyze the classifier’s results through the 3 different methods like Bagging, Boosting, Stacking, added on the base learners to improve the\u0000 proposed work. Based on the performance, the base learners’ performance is larger than the input dataset. The base learner’s parameters are essential to calculate the accuracy of skin disease prediction performance. The performance of the proposed method will take and compare to\u0000 each base learner, and the performance shows the accurate skin disease prediction improvement with other existing methods.","PeriodicalId":393031,"journal":{"name":"J. Medical Imaging Health Informatics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114400116","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 liver is a vital organ in human body. Liver performs an important function including metabolism, digestion, and detoxification. Liver is a significant organ in an abdomen, and is connected to the nearby organ such as spleen, pancreas, gallbladder, abdomen, and gut through blood vessels. Specific approaches such as image gradient and region growing are not quite reliable for the segmentation of the liver tumor. A level-set approach is evaluated in this paper compared with the active contour approach of segmentation of the liver imaging from the image of the CT abdomen and Unified level set method, spatial Fuzzy C-means method for segmenting tumor from segmented liver images is appraised. The proposed approach is implemented by using the 3DIRCADB dataset available to the public as well as non-public datasets taken from Arthi Hospital, Chennai and Tirunelveli scanning centre. For validating the system based on the diverse quantitative measures, including space overlap, coefficient of similarity, Jaccard indices, using ground truth images, which are available in the public data set 3DIRCADB and the expert segmentation results which are manually identified by the clinical partner for nonpublic datasets. The analysis of the algorithm shows the better results for segmenting liver using level set system and spatial segmentation of Fuzzy C means of the tumor segmentation.
{"title":"An Automatic Detection of Liver Tumor from CT Abdominal Images - A Comparative Approach","authors":"R. Devi, A. Shenbagavalli","doi":"10.1166/jmihi.2021.3875","DOIUrl":"https://doi.org/10.1166/jmihi.2021.3875","url":null,"abstract":"The liver is a vital organ in human body. Liver performs an important function including metabolism, digestion, and detoxification. Liver is a significant organ in an abdomen, and is connected to the nearby organ such as spleen, pancreas, gallbladder, abdomen, and gut through blood\u0000 vessels. Specific approaches such as image gradient and region growing are not quite reliable for the segmentation of the liver tumor. A level-set approach is evaluated in this paper compared with the active contour approach of segmentation of the liver imaging from the image of the CT abdomen\u0000 and Unified level set method, spatial Fuzzy C-means method for segmenting tumor from segmented liver images is appraised. The proposed approach is implemented by using the 3DIRCADB dataset available to the public as well as non-public datasets taken from Arthi Hospital, Chennai and Tirunelveli\u0000 scanning centre. For validating the system based on the diverse quantitative measures, including space overlap, coefficient of similarity, Jaccard indices, using ground truth images, which are available in the public data set 3DIRCADB and the expert segmentation results which are manually\u0000 identified by the clinical partner for nonpublic datasets. The analysis of the algorithm shows the better results for segmenting liver using level set system and spatial segmentation of Fuzzy C means of the tumor segmentation.","PeriodicalId":393031,"journal":{"name":"J. Medical Imaging Health Informatics","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134428890","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}
Among women, cervical cancer is the commonest and the most treatable and preventable type of cancer. In most cases, cervical cancer begins as precancerous changes which gradually develop into cancer. Pap smear is widely used for cervical cancer diagnosis. Cell analysis is a time-consuming and cumbersome job; thus, an automatic detecting framework is proposed. Wavelet transforms offer the associated coefficients as the input image data representation, used as feature vectors. Artificial Neural Networks (ANNs) have outstanding attributes such as enhanced input-to-output mapping, non-linearity, fault tolerance, adaptively, and self-learning. Classification of cervical cancers employs neural network systems that have a huge role in most applications related to image processing. For application in diverse fields such as bioinformatics and pattern recognition, most researchers choose ensemble classifiers. A spatial-frequency feature ensemble has been proposed in this work to identify cervical dysplasia from images of Pap smears.
{"title":"A Spatial-Frequency Feature Ensemble for Detecting Cervical Dysplasia from Pap Smear Images","authors":"K. Deepa, S. Thilagamani","doi":"10.1166/jmihi.2021.3869","DOIUrl":"https://doi.org/10.1166/jmihi.2021.3869","url":null,"abstract":"Among women, cervical cancer is the commonest and the most treatable and preventable type of cancer. In most cases, cervical cancer begins as precancerous changes which gradually develop into cancer. Pap smear is widely used for cervical cancer diagnosis. Cell analysis is a time-consuming\u0000 and cumbersome job; thus, an automatic detecting framework is proposed. Wavelet transforms offer the associated coefficients as the input image data representation, used as feature vectors. Artificial Neural Networks (ANNs) have outstanding attributes such as enhanced input-to-output mapping,\u0000 non-linearity, fault tolerance, adaptively, and self-learning. Classification of cervical cancers employs neural network systems that have a huge role in most applications related to image processing. For application in diverse fields such as bioinformatics and pattern recognition, most researchers\u0000 choose ensemble classifiers. A spatial-frequency feature ensemble has been proposed in this work to identify cervical dysplasia from images of Pap smears.","PeriodicalId":393031,"journal":{"name":"J. Medical Imaging Health Informatics","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114171875","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}
Video retrieval recognition is a linear characterized action constituted by many frame similarity-based videos. This medical video recognition and classification can be a great extent in medical research, such as Endoscopic, radiological, pathological, and applied health informatics. General Video Retrieval Recognition (GVRR) cannot address a problem with recognition alone. GVRR can be solving the Multi-Input-Multi-Output (MIMO) interface mixed video retrieval system. To generalize the conventional video retrieval interface like Multi-user MIMO, WiMAX MIMO, single-user MIMO, several types of research made excused. In fine-tuning existing video retrieval, this research gives the authentic procedure for a frame-based cognitive operation called Secure Approximation and sTability Based Secure Video Retrieval recognition (SAT-SR) recognition proposed. In this research article, the process of recognition has three processes generalized by the video retrieval system. Initially, the virtual dissection and connection weights of input video were established using the mathematical and numerical analysis of interpolation estimation. Secondly, the interpolation approximation and activation function were figured out using the Open Mcrypt Stimulus (oMs) for video security fragments. Similarly, systematic investigations are accomplished for approximation error computation. The result for this widely circulated utilization of three processes on the video retrieval recognition prevents the occurrence of the cybercrime abuse of stored video registers. The proposed technique was used to identify the virtual dissection, interpolation, and activation function for decoding the videos. Using this information, the abusers identified cybercrime rate might be reduced considerably.
{"title":"Medical Surgical Video Recognition and Retrieval Based on Novel Unified Approximation","authors":"B. Sathiyaprasad, Koushik Seetharaman","doi":"10.1166/jmihi.2021.3874","DOIUrl":"https://doi.org/10.1166/jmihi.2021.3874","url":null,"abstract":"Video retrieval recognition is a linear characterized action constituted by many frame similarity-based videos. This medical video recognition and classification can be a great extent in medical research, such as Endoscopic, radiological, pathological, and applied health informatics.\u0000 General Video Retrieval Recognition (GVRR) cannot address a problem with recognition alone. GVRR can be solving the Multi-Input-Multi-Output (MIMO) interface mixed video retrieval system. To generalize the conventional video retrieval interface like Multi-user MIMO, WiMAX MIMO, single-user\u0000 MIMO, several types of research made excused. In fine-tuning existing video retrieval, this research gives the authentic procedure for a frame-based cognitive operation called Secure Approximation and sTability Based Secure Video Retrieval recognition (SAT-SR) recognition proposed. In this\u0000 research article, the process of recognition has three processes generalized by the video retrieval system. Initially, the virtual dissection and connection weights of input video were established using the mathematical and numerical analysis of interpolation estimation. Secondly, the interpolation\u0000 approximation and activation function were figured out using the Open Mcrypt Stimulus (oMs) for video security fragments. Similarly, systematic investigations are accomplished for approximation error computation. The result for this widely circulated utilization of three processes on the video\u0000 retrieval recognition prevents the occurrence of the cybercrime abuse of stored video registers. The proposed technique was used to identify the virtual dissection, interpolation, and activation function for decoding the videos. Using this information, the abusers identified cybercrime rate\u0000 might be reduced considerably.","PeriodicalId":393031,"journal":{"name":"J. Medical Imaging Health Informatics","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124280415","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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