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Int. J. Medical Eng. Informatics最新文献

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In-depth analysis of neural network ensembles for early detection method of diabetes disease 深度分析神经网络集成在糖尿病疾病早期检测中的应用
Int. J. Medical Eng. Informatics Pub Date : 2018-10-04 DOI: 10.1504/IJMEI.2018.10014083
Bayu Adhi Tama, K. Rhee
{"title":"In-depth analysis of neural network ensembles for early detection method of diabetes disease","authors":"Bayu Adhi Tama, K. Rhee","doi":"10.1504/IJMEI.2018.10014083","DOIUrl":"https://doi.org/10.1504/IJMEI.2018.10014083","url":null,"abstract":"Lifestyle-driven disease such as diabetes mellitus has become a serious health problem worldwide. We propose the fusion of neural network-based classifiers, i.e., neural network and support vector machine to assist in early detection of diabetes mellitus. These classifiers are combined to produce the final prediction. However, when considering a number of classifiers in the pool, the selection of combination rule is not easy to understand. The aim of this paper is to investigate the performance of different combination rules, including several single classifiers involved in the ensemble. We use various performance metrics and validation tests to assess the performance of these classifiers using a real-world dataset. Finally, among the classifiers we evaluate their performance differences using statistical significant test. The experimental results indicate that combination rule with average voting scheme is the best performer compared with other combination rules and single classifiers in the ensemble.","PeriodicalId":193362,"journal":{"name":"Int. J. Medical Eng. Informatics","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125051374","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}
引用次数: 2
Analysis of texture for classification of breast cancer on mammogram images 乳腺x光图像纹理分类分析
Int. J. Medical Eng. Informatics Pub Date : 2018-10-04 DOI: 10.1504/IJMEI.2018.10014086
H. A. Nugroho, H. Fajrin, I. Soesanti, R. L. Budiani
{"title":"Analysis of texture for classification of breast cancer on mammogram images","authors":"H. A. Nugroho, H. Fajrin, I. Soesanti, R. L. Budiani","doi":"10.1504/IJMEI.2018.10014086","DOIUrl":"https://doi.org/10.1504/IJMEI.2018.10014086","url":null,"abstract":"Breast cancer is a top cancer among women in the world. In conventional method, breast cancer can be detected by a medical expertise observation on patient's mammogram images. However, this method could lead to misdiagnose in distinguishing an interest object with naked eyes due to low quality of images. This research aims to classify mammogram images into three classes, i.e. normal, benign and malignant based on texture features. Some pre-processing techniques were involved, including removing the artefacts, cropping breast area, contrast enhancement and smoothing with median filter. Afterwards, some texture features were extracted followed by classification process by using multi-layer perceptron (MLP) classifier. Classification of normal and abnormal successfully achieved an accuracy of 98.33%, sensitivity of 100% and specificity of 97.5%. Whereas, for classification of three classes (normal, benign and malignant) achieved an accuracy of 90%, sensitivity of 85% and specificity of 87.5%.","PeriodicalId":193362,"journal":{"name":"Int. J. Medical Eng. Informatics","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116473695","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}
引用次数: 1
Neuro-fuzzy implementation for cervical lesions screening in commercial sex workers 神经模糊技术在商业性工作者宫颈病变筛查中的应用
Int. J. Medical Eng. Informatics Pub Date : 2018-10-04 DOI: 10.1504/IJMEI.2018.10014084
Bolaji Efosa Odigie, P. Achukwu, M. E. Bello
{"title":"Neuro-fuzzy implementation for cervical lesions screening in commercial sex workers","authors":"Bolaji Efosa Odigie, P. Achukwu, M. E. Bello","doi":"10.1504/IJMEI.2018.10014084","DOIUrl":"https://doi.org/10.1504/IJMEI.2018.10014084","url":null,"abstract":"This study proposed a neuro-fuzzy model for cervical lesions (CL) detection in commercial sex workers. Our aim is to formulate an understandable and practicable model for CL screening of commercial sex workers (CSWs) operating in rural communities in Edo State, Nigeria. The specific objective is to confirm the levels of precision of the neuro-fuzzy model using liquid-based cytology (LBC) method. The adaptive neuro-fuzzy inference system (ANFIS) implementation was used for screening and LBC techniques for confirmation of the ANFIS outcomes. The classification performance of ANFIS model had 98.7% precision with a training error of 1.1652 and basic testing error 1.255. LBC showed eight cases of CL (15.4%) from 259 prostitutes and was age and duration of commercial sex-dependent (P < 0.05). The present ANFIS implemented model is excellent for routine screening of the CSWs, while LBC remains a gold standard for CL diagnosis worldwide.","PeriodicalId":193362,"journal":{"name":"Int. J. Medical Eng. Informatics","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121446914","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}
引用次数: 3
Classification of heart rhythm disorders using instructive features and artificial neural networks 心律失常的指导性特征及人工神经网络分类
Int. J. Medical Eng. Informatics Pub Date : 2018-10-04 DOI: 10.1504/IJMEI.2018.10014085
Santanu Sahoo, Priti Das, P. Biswal, S. Sabut
{"title":"Classification of heart rhythm disorders using instructive features and artificial neural networks","authors":"Santanu Sahoo, Priti Das, P. Biswal, S. Sabut","doi":"10.1504/IJMEI.2018.10014085","DOIUrl":"https://doi.org/10.1504/IJMEI.2018.10014085","url":null,"abstract":"Accurate detection of the heart rhythm disorders at an early stage is helpful for improving survival rate. This paper presents an automated detection and classification methods of cardiac arrhythmia by time-frequency analysis of the recorded ECG signals from MIT-BIH database. The discrete wavelet transform has been used to eliminate noises in order to enhance the quality of signals and adaptive thresholding-based Hilbert transform has been used to find precise R-peaks. Temporal, morphological and statistical features were extracted from each heartbeat and has been used as input to the classifier to detect five cardiac arrhythmia beats. The results show less detection error rate of 0.17% in detecting QRS complex. The MLP-BP, RBF-NN, and the PNN classifiers provide an average accuracy of 98.72%, 99.77% and 99.16% respectively. The result indicates the efficiency of the proposed method in classifying ECG beats which is useful in diagnosis of cardiac arrhythmias.","PeriodicalId":193362,"journal":{"name":"Int. J. Medical Eng. Informatics","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117108879","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}
引用次数: 5
SEPHYRES 2: applying semantic-pseudo-fuzzy methods in medical diagnostic ontologies 语义-伪模糊方法在医学诊断本体中的应用
Int. J. Medical Eng. Informatics Pub Date : 2018-07-27 DOI: 10.1504/IJMEI.2018.10013914
Ali Sanaeifar, M. Tara, A. Faraahi, Bibimasoumeh Mir Mousavi, M. Ahadi, A. Bahari
{"title":"SEPHYRES 2: applying semantic-pseudo-fuzzy methods in medical diagnostic ontologies","authors":"Ali Sanaeifar, M. Tara, A. Faraahi, Bibimasoumeh Mir Mousavi, M. Ahadi, A. Bahari","doi":"10.1504/IJMEI.2018.10013914","DOIUrl":"https://doi.org/10.1504/IJMEI.2018.10013914","url":null,"abstract":"To date, ontology-based medical diagnostic systems have not incorporated complete descriptions of diseases and their semantic relations with signs and symptoms. In SEPHYRES 1, a pain-focused-only solution was proposed which applied not only general semantic reasoners, but also weight spreading techniques. Proceeding the research, we developed the SEPHYRES knowledge base to address all signs, symptoms and complex relations, including similar terms, terms with the variant generality level, composed terms, terms which include several other terms based on medical diagnostic criteria. The evaluation outcomes, in terms used in patient's descriptive history in both of the MEDSCAPE and PubMed case studies, showed that the recall amount of system-oriented evaluation was about 90% provided that just top ten results were considered. Furthermore, the Wilcoxon signed-ranked test between SEPHYRES 2 and the best symptom checker, Isabel engine power, showed that the SEPHYRES 2 significantly improved the matching process of the patient's disease profiles.","PeriodicalId":193362,"journal":{"name":"Int. J. Medical Eng. Informatics","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121974869","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}
引用次数: 1
Activity monitoring and meal tracking for cardiac rehabilitation patients 心脏康复患者的活动监测和膳食跟踪
Int. J. Medical Eng. Informatics Pub Date : 2018-07-27 DOI: 10.1504/IJMEI.2018.10006898
Usama Pervaiz, Saed Khawaldeh, Tajwar Abrar Aleef, Vu Minh Hoang, Y. Hagos
{"title":"Activity monitoring and meal tracking for cardiac rehabilitation patients","authors":"Usama Pervaiz, Saed Khawaldeh, Tajwar Abrar Aleef, Vu Minh Hoang, Y. Hagos","doi":"10.1504/IJMEI.2018.10006898","DOIUrl":"https://doi.org/10.1504/IJMEI.2018.10006898","url":null,"abstract":"Heart disease has been ranked as the first cause for death in Pakistan along with being the leading cause of death worldwide (https://www.cdc.gov). Recovering heart patients need to monitor their diet and activity routines to mitigate chances of another occurrence (https://www.heart.org). The proposed application aims to provide a platform that would help patients to keep track of their activity, alongside allowing the patients to keep track of their diet; which will act as fitness monitoring for cardiac rehabilitation patients. Further more, activity classification algorithm used only seven features to shows classification into five different classes with accuracy of 98% in real time. Also, meal tracking module is designed to support the cardiac patients with timely feedback and notifications about their dietary routine.","PeriodicalId":193362,"journal":{"name":"Int. J. Medical Eng. Informatics","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128187142","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}
引用次数: 3
Towards understanding the aetiology of high myopic strabismus using mechanical analysis and finite element modelling 利用力学分析和有限元模型来了解高度近视斜视的病因
Int. J. Medical Eng. Informatics Pub Date : 2018-07-27 DOI: 10.1504/IJMEI.2018.10013911
Haipeng Liu, Yinglan Gong, Zhiqing Chen, S. Pardhan, R. Mootanah, L. Xia, D. Zheng
{"title":"Towards understanding the aetiology of high myopic strabismus using mechanical analysis and finite element modelling","authors":"Haipeng Liu, Yinglan Gong, Zhiqing Chen, S. Pardhan, R. Mootanah, L. Xia, D. Zheng","doi":"10.1504/IJMEI.2018.10013911","DOIUrl":"https://doi.org/10.1504/IJMEI.2018.10013911","url":null,"abstract":"It has been widely accepted that the pathology of high myopic esotropia, a special form of strabismus, is still not fully understood. In this study, the mechanical analysis and finite element analysis (FEA) of the oculomotor system was based on clinical MRI data and applied to examine the physiological hypotheses of extra-ocular muscle obliquity and deformation respectively. Our mechanical analysis indicated that the muscular obliquity is not the dominated cause of high myopic strabismus. Next, by simulating the effect of different forces applied to the cross section of each extra-ocular rectus muscles, the corresponding eyeball rotations were quantified on normal eyes, and high myopic eyes with and without strabismus. The model suggests that the limitation of rotation in high myopic strabismic eyes is mainly caused by the extra-ocular muscle deformation instead of, but related with, its obliquity, providing a better understanding of the aetiology of high myopic strabismus. To the best of our knowledge, this is the first mechanical and FEA model developed from clinical data to investigate the aetiology of high myopic strabismus, providing important tools for future studies.","PeriodicalId":193362,"journal":{"name":"Int. J. Medical Eng. Informatics","volume":"302 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130050035","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}
引用次数: 0
SMOTE and ABC optimised RBF network for coping with imbalanced class in EEG signal classification SMOTE和ABC优化RBF网络处理脑电信号分类中的不平衡类
Int. J. Medical Eng. Informatics Pub Date : 2018-07-27 DOI: 10.1504/IJMEI.2018.10013912
S. Satapathy, Satchidananda Dehuri, A. Jagadev
{"title":"SMOTE and ABC optimised RBF network for coping with imbalanced class in EEG signal classification","authors":"S. Satapathy, Satchidananda Dehuri, A. Jagadev","doi":"10.1504/IJMEI.2018.10013912","DOIUrl":"https://doi.org/10.1504/IJMEI.2018.10013912","url":null,"abstract":"This paper proposes a novel approach for coping with imbalanced class problem by combining the best attribute of synthetic minority over-sampling technique (SMOTE) and artificial bee colony optimised radial basis function neural networks to identify epileptic seizure from electroencephalography (EEG) signal. EEG is the recording of electrical activity in brain. Careful analysis of these recordings can provide valuable information and understanding the mechanisms of several brain disorder diseases such as epilepsy. Since epileptic seizures occur irregularly and unpredictably, automatic seizure detection in EEG recordings is highly required. We have used discrete wavelet transform (DWT) technique for extraction of potential features from the signal. For classification of these signals into two classes, we have trained the RBFN by a modified version of ABC algorithm (MABC). In this work, we realise, this two class classification problem is highly imbalanced i.e., the instances in one class known as majority class outnumber the instances of other class called the minority class. The SMOTE is first applied to generate synthetic instances in the positive class to balance the training data set. Using the resulting balanced dataset, the MABC optimised RBF network is then constructed to identify the epileptic seizure.","PeriodicalId":193362,"journal":{"name":"Int. J. Medical Eng. Informatics","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132582390","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}
引用次数: 2
Parametric electrical impedance tomography for monitoring bone mineral density in the spine using 3D human model 参数电阻抗断层扫描监测骨密度在脊柱使用三维人体模型
Int. J. Medical Eng. Informatics Pub Date : 2018-07-27 DOI: 10.1504/IJMEI.2018.10013915
Neta Naimark, S. Abboud, M. Arad
{"title":"Parametric electrical impedance tomography for monitoring bone mineral density in the spine using 3D human model","authors":"Neta Naimark, S. Abboud, M. Arad","doi":"10.1504/IJMEI.2018.10013915","DOIUrl":"https://doi.org/10.1504/IJMEI.2018.10013915","url":null,"abstract":"Monitoring methods of bone mineral density (BMD), the standard measure for osteoporosis diagnosis, are both costly and complex. Since changes in bone permittivity and conductivity values occur due to changes in BMD, they can be used as a simple and inexpensive tool for monitoring BMD. In this work the parametric electrical impedance tomography (pEIT) method for monitoring BMD in the spine using 3D human model is theoretically evaluated. Numerical solver on the forward problem in 3D is used for computing electric potential measured on body surface. Varied spinal BMD are simulated by varying bone relative permittivity and conductivity values which represent different disease stages. The inverse problem is solved by creating a lookup-table of different BMD values.","PeriodicalId":193362,"journal":{"name":"Int. J. Medical Eng. Informatics","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126716254","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}
引用次数: 1
Force evaluation and stress distribution at possible weight and structure of femur bone in pelvis frame while standing 站立时骨盆框架内股骨可能重量和结构的受力评估及应力分布
Int. J. Medical Eng. Informatics Pub Date : 2018-07-27 DOI: 10.1504/IJMEI.2018.10013913
Bhaskar Kumar Madeti, C. S. Rao, S. K. S. S. R. Bollapragada
{"title":"Force evaluation and stress distribution at possible weight and structure of femur bone in pelvis frame while standing","authors":"Bhaskar Kumar Madeti, C. S. Rao, S. K. S. S. R. Bollapragada","doi":"10.1504/IJMEI.2018.10013913","DOIUrl":"https://doi.org/10.1504/IJMEI.2018.10013913","url":null,"abstract":"The present paper gives a clear idea of hip joint, it is very complicated structure here we resolve the forces acting on hip joint by using Lami's theorem, by using the geometry from the CT scan the tensile force of ligament and axial compressive forces across the femoral head are obtained at various possible positions of pelvis frame while standing. Analysis is done for a person whose weight varies from 600 N to 1,500 N while standing, it is also extended to finite element analysis, stress distribution and deformation is noticed on the surface of femur. Factor of safety is also obtained.","PeriodicalId":193362,"journal":{"name":"Int. J. Medical Eng. Informatics","volume":"1768 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129521822","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}
引用次数: 3
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