The establishment of a scientific and complete intelligent medical information analysis application model is of great significance to promote the application of intelligent medical information. Aiming at the deficiencies of Artificial Fish School Algorithm (AFSA) in iterative convergence speed, low optimization accuracy, and Particle Swarm Optimization (PSO) algorithm easily falling into local extremes, this paper combines AFSA and PSO algorithms. We use the fast local convergence ability of the PSO algorithm to overcome the shortcomings of the AFSA algorithm’s low solution accuracy and slow convergence speed. In the classification stage, we try to apply machine learning technology to classify the labeled feature vectors, evaluate and analyze the performance of these two machine learning algorithms in intelligent medical diagnosis auxiliary applications, and use today’s popular deep learning classification methods (i.e., intelligently optimized text classification model) and machine learning classification method to compare the classification effect, evaluate and analyze the applicability of the classification model in the auxiliary application of intelligent medical diagnosis. The experimental results show that the accuracy rate of applying the machine learning method to the judgment of the type of disease reaches more than 90%, which is fully in line with the disease judgment of the patient.
{"title":"Optimization of Patient Health Management Mechanism Under Intelligent Medical Information System","authors":"Lifang Zheng, Weixia Liu, Hangying Chen","doi":"10.1166/jmihi.2022.3782","DOIUrl":"https://doi.org/10.1166/jmihi.2022.3782","url":null,"abstract":"The establishment of a scientific and complete intelligent medical information analysis application model is of great significance to promote the application of intelligent medical information. Aiming at the deficiencies of Artificial Fish School Algorithm (AFSA) in iterative convergence\u0000 speed, low optimization accuracy, and Particle Swarm Optimization (PSO) algorithm easily falling into local extremes, this paper combines AFSA and PSO algorithms. We use the fast local convergence ability of the PSO algorithm to overcome the shortcomings of the AFSA algorithm’s low solution\u0000 accuracy and slow convergence speed. In the classification stage, we try to apply machine learning technology to classify the labeled feature vectors, evaluate and analyze the performance of these two machine learning algorithms in intelligent medical diagnosis auxiliary applications, and\u0000 use today’s popular deep learning classification methods (i.e., intelligently optimized text classification model) and machine learning classification method to compare the classification effect, evaluate and analyze the applicability of the classification model in the auxiliary application\u0000 of intelligent medical diagnosis. The experimental results show that the accuracy rate of applying the machine learning method to the judgment of the type of disease reaches more than 90%, which is fully in line with the disease judgment of the patient.","PeriodicalId":393031,"journal":{"name":"J. Medical Imaging Health Informatics","volume":"193 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115715499","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}
One of the major complicated issues for extensive term diabetic aspirant is diabetic retinopathy (DR) which is an eye retinal syndrome, leads to blindness. The presence of exudates detects the disease, which can be prevented in the early stages by regular screening. Exudates can be automatically detected through inspecting digital retinal image. To detect the exudates for diagnosis the author proposed an algorithm called K-means Kernel support vector machine Radial basis function (KKR) approach, by the following main stages: extracting vessel and removal of optic disc followed by pre-processing, exudates detection and post processing. Wavelet dependent edge enhancement is used for dark portion separation of exudates in the retinal image by optically designed Wideband bandpass filter. Wavelet toolbox of MATLAB 2018a is used in this KKR algorithm. Statistical and structural texture features can be obtained using K-means segmentation process by integrating Local Binary Pattern (LBP) with Region Of Interest (ROI). Some features are selected and used Neural Network along with Radial Basis Function (RBF) to classify further. The KKR algorithm uses 80 fundus images from DIARETDB1 database and parameters are analyzed such as specificity, sensitivity and accuracy. The results obtained from proposed KKR algorithm have specificity of 81.57%, sensitivity of 87.56% and accuracy of 97.94% respectively.
糖尿病视网膜病变(DR)是一种导致失明的视网膜综合征，是长期糖尿病患者的主要复杂问题之一。渗出物的存在可以发现疾病，这可以通过定期筛查在早期阶段预防。通过检查数字视网膜图像，可以自动检测渗出物。为了检测渗出物进行诊断，作者提出了一种k -均值核支持向量机径向基函数(KKR)算法，该算法主要分为提取血管、去除视盘、预处理、渗出物检测和后处理三个阶段。利用光学设计的宽带带通滤波器，利用小波相关边缘增强对视网膜图像中渗出物的暗部进行分离。该KKR算法使用了MATLAB 2018a的小波工具箱。利用局部二值模式(Local Binary Pattern, LBP)和感兴趣区域(Region Of Interest, ROI)相结合的K-means分割方法，可以得到统计和结构纹理特征。选取部分特征，利用神经网络结合径向基函数(RBF)进行进一步分类。KKR算法使用来自DIARETDB1数据库的80张眼底图像，并对特异性、敏感性和准确性等参数进行分析。KKR算法的特异度为81.57%，灵敏度为87.56%，准确率为97.94%。
{"title":"Detection of Diabetic Retinopathy Using Discrete Wavelet Transform with Discrete Meyer in Retinal Images","authors":"G. Ramani, T. Menakadevi","doi":"10.1166/jmihi.2022.3926","DOIUrl":"https://doi.org/10.1166/jmihi.2022.3926","url":null,"abstract":"One of the major complicated issues for extensive term diabetic aspirant is diabetic retinopathy (DR) which is an eye retinal syndrome, leads to blindness. The presence of exudates detects the disease, which can be prevented in the early stages by regular screening. Exudates can be\u0000 automatically detected through inspecting digital retinal image. To detect the exudates for diagnosis the author proposed an algorithm called K-means Kernel support vector machine Radial basis function (KKR) approach, by the following main stages: extracting vessel and removal of optic\u0000 disc followed by pre-processing, exudates detection and post processing. Wavelet dependent edge enhancement is used for dark portion separation of exudates in the retinal image by optically designed Wideband bandpass filter. Wavelet toolbox of MATLAB 2018a is used in this KKR algorithm. Statistical\u0000 and structural texture features can be obtained using K-means segmentation process by integrating Local Binary Pattern (LBP) with Region Of Interest (ROI). Some features are selected and used Neural Network along with Radial Basis Function (RBF) to classify further. The KKR algorithm\u0000 uses 80 fundus images from DIARETDB1 database and parameters are analyzed such as specificity, sensitivity and accuracy. The results obtained from proposed KKR algorithm have specificity of 81.57%, sensitivity of 87.56% and accuracy of 97.94% respectively.","PeriodicalId":393031,"journal":{"name":"J. Medical Imaging Health Informatics","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117213632","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}
Diabetic Retinopathy (DR) is a complicated disease of diabetes, which specifically affects the retina. The human-intensive analysis mechanism of DR infected retina are likely to diagnose wrongly compared to computer-intensive diagnosis systems. In this paper, in order to aid the computer based approach for the diagnosis of DR, a model based on machine learning algorithm is proposed. The nucleotides of the human retina are processed with the help of signal processing methodologies. A speed efficient Fast Fourier transform is proposed to work out the FFT of huge amount of samples with higher pace. The improvement in speed is achieved in 98% of the samples. The prediction parameters, derived from these samples are utilized to classify the healthy retina sequence and an infected retina. In this study, Fine Tree, KNN Fine, Weighted KNN, Ensemble Bagged Trees and Ensemble Subspace KNN classifiers are employed to build the models. The simulated results using MATLAB software show that the accuracy is 98% which is better than image processing based methods which were used earlier. The performance parameters such as sensitivity and specificity are determined for each model. The faithfulness of the model is studied by deriving the ROC Curve.
{"title":"Speed Efficient Fast Fourier Transform for Signal Processing of Nucleotides to Detect Diabetic Retinopathy Using Machine Learning","authors":"C. Saravanakumar, N. Bhanu","doi":"10.1166/jmihi.2022.3922","DOIUrl":"https://doi.org/10.1166/jmihi.2022.3922","url":null,"abstract":"Diabetic Retinopathy (DR) is a complicated disease of diabetes, which specifically affects the retina. The human-intensive analysis mechanism of DR infected retina are likely to diagnose wrongly compared to computer-intensive diagnosis systems. In this paper, in order to aid the computer\u0000 based approach for the diagnosis of DR, a model based on machine learning algorithm is proposed. The nucleotides of the human retina are processed with the help of signal processing methodologies. A speed efficient Fast Fourier transform is proposed to work out the FFT of huge amount of samples\u0000 with higher pace. The improvement in speed is achieved in 98% of the samples. The prediction parameters, derived from these samples are utilized to classify the healthy retina sequence and an infected retina. In this study, Fine Tree, KNN Fine, Weighted KNN, Ensemble Bagged Trees and Ensemble\u0000 Subspace KNN classifiers are employed to build the models. The simulated results using MATLAB software show that the accuracy is 98% which is better than image processing based methods which were used earlier. The performance parameters such as sensitivity and specificity are determined for\u0000 each model. The faithfulness of the model is studied by deriving the ROC Curve.","PeriodicalId":393031,"journal":{"name":"J. Medical Imaging Health Informatics","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131934397","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}
Early detection of cataract is considered as an important solution to prevent vision loss. An automatic detection of cataract is proposed in this work with the help of histogram approach. In the beginning, noises occur in an image which is also referred to as impulse noise. To eliminate this noise a non-linear type of median filter is matched especially for the morphological filter. These filtering methods help to extract the content of the image by edge detection and segmentation. The quality of the image is evaluated the image enhancing can be obtained by a histogram approach. A normalization method can be used to enhance the image which is also called Contrast stretching. To make morphological functions effective a top-hat filter is used to segment the cataract part in the given image. Nakagami distributions are usually used for extracting required important information of ultrasound details by matching histograms from the radio frequency signals. The extracted information from the Nakagami distribution is obtained by parameter values. The recent techniques used to improve the given image quality in histogram modification method are done by Intentional Camera Movement (ICM) and Unintentional Camera Movement (UCM) to recognize the real image more precisely. In the proposed method the result shows the noise reduction and a better contrast in the output image through parameters values such as Mean Squared Error (MSE) obtained as 17.23 and Peak-Signal-to-Noise Ratio (PSNR) obtained as 35.8.
{"title":"A Novel Method for Cataract Detection and Segmentation Using Nakagami Distribution","authors":"Martin Joel Rathnam, M. Christ","doi":"10.1166/jmihi.2022.3924","DOIUrl":"https://doi.org/10.1166/jmihi.2022.3924","url":null,"abstract":"Early detection of cataract is considered as an important solution to prevent vision loss. An automatic detection of cataract is proposed in this work with the help of histogram approach. In the beginning, noises occur in an image which is also referred to as impulse noise. To eliminate\u0000 this noise a non-linear type of median filter is matched especially for the morphological filter. These filtering methods help to extract the content of the image by edge detection and segmentation. The quality of the image is evaluated the image enhancing can be obtained by a histogram approach.\u0000 A normalization method can be used to enhance the image which is also called Contrast stretching. To make morphological functions effective a top-hat filter is used to segment the cataract part in the given image. Nakagami distributions are usually used for extracting required important information\u0000 of ultrasound details by matching histograms from the radio frequency signals. The extracted information from the Nakagami distribution is obtained by parameter values. The recent techniques used to improve the given image quality in histogram modification method are done by Intentional Camera\u0000 Movement (ICM) and Unintentional Camera Movement (UCM) to recognize the real image more precisely. In the proposed method the result shows the noise reduction and a better contrast in the output image through parameters values such as Mean Squared Error (MSE) obtained as 17.23 and Peak-Signal-to-Noise\u0000 Ratio (PSNR) obtained as 35.8.","PeriodicalId":393031,"journal":{"name":"J. Medical Imaging Health Informatics","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128964926","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 demand in breast cancer’s early detection and diagnosis over the last few decade has given a new research avenues. For an individual who is suffered from breast cancer, a successful treatment plan can be specified if early stage diagnosis of non-communicable disease is done as stated by world health organization (WHO). Around the world, mortality can be reduced by cure disease’s early diagnosis. For breast cancer’s early detection and to detect other abnormalities of human breast tissue, digital mammogram is used as a most popular screening method. Early detection is assisted by periodic clinical check-ups and self-tests and survival chance is significantly enhanced by it. For mammograms (MGs), deep learning (DL) methods are investigated by researchers due to traditional computer-aided detection (CAD) systems limitations and breast cancer’s early detection’s extreme importance and patients false diagnosis high impact. So, there is need to have a noninvasive cancer detection system which is efficient, accurate, fast and robust. There are two process in proposed work, Histogram Rehabilitated Local Contrast Enhancement (HRLCE) technique is used in initial process for contrast enhancement with two processing stages. Contrast enhancements potentiality is enhanced while preserving image’s local details by this technique. So, for cancer classification, Particle Swarm Optimization (PSO) and stacked auto encoders (SAE) combined with framework based on DNN called SAE-PSO-DNN Model is used. The SAE-DNN parameters with two hidden layers are tuned using PSO and Limited-memory BFGS (LBFGS) is used as a technique for reducing features. Specificity, sensitivity, normalized root mean square erro (NRMSE), accuracy parameters are used for evaluating SAE-PSO-DNN models results. Around 92% of accurate results are produced by SAE-PSO-DNN model as shown in experimentation results, which is far better than Convolutional Neural Network (CNN) as well as Support Vector Machine (SVM) techniques.
{"title":"An Early Breast Cancer Detection System Using Stacked Auto Encoder Deep Neural Network with Particle Swarm Optimization Based Classification Method","authors":"K. Sangeetha, S. Prakash","doi":"10.1166/jmihi.2021.3886","DOIUrl":"https://doi.org/10.1166/jmihi.2021.3886","url":null,"abstract":"The demand in breast cancer’s early detection and diagnosis over the last few decade has given a new research avenues. For an individual who is suffered from breast cancer, a successful treatment plan can be specified if early stage diagnosis of non-communicable disease is done\u0000 as stated by world health organization (WHO). Around the world, mortality can be reduced by cure disease’s early diagnosis. For breast cancer’s early detection and to detect other abnormalities of human breast tissue, digital mammogram is used as a most popular screening method.\u0000 Early detection is assisted by periodic clinical check-ups and self-tests and survival chance is significantly enhanced by it. For mammograms (MGs), deep learning (DL) methods are investigated by researchers due to traditional computer-aided detection (CAD) systems limitations and breast cancer’s\u0000 early detection’s extreme importance and patients false diagnosis high impact. So, there is need to have a noninvasive cancer detection system which is efficient, accurate, fast and robust. There are two process in proposed work, Histogram Rehabilitated Local Contrast Enhancement (HRLCE)\u0000 technique is used in initial process for contrast enhancement with two processing stages. Contrast enhancements potentiality is enhanced while preserving image’s local details by this technique. So, for cancer classification, Particle Swarm Optimization (PSO) and stacked auto encoders\u0000 (SAE) combined with framework based on DNN called SAE-PSO-DNN Model is used. The SAE-DNN parameters with two hidden layers are tuned using PSO and Limited-memory BFGS (LBFGS) is used as a technique for reducing features. Specificity, sensitivity, normalized root mean square erro (NRMSE), accuracy\u0000 parameters are used for evaluating SAE-PSO-DNN models results. Around 92% of accurate results are produced by SAE-PSO-DNN model as shown in experimentation results, which is far better than Convolutional Neural Network (CNN) as well as Support Vector Machine (SVM) techniques.","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":"129555344","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}
Deep learning plays a key role in medical image processing. One of the applications of deep learning models in this domain is bone fracture detection from X-ray images. Convolutional neural network and its variants are used in wide range of medical image processing applications. MURA Dataset is commonly used in various studies that detect bone fractures and this work also uses that dataset, in specific the Humerus bone radiograph images. The humerus dataset in the MURA dataset contains both images with fracture and without fracture. The image with fracture includes images with metals which are removed in this work. Experimental analysis was made with two variants of convolutional neural network, DenseNet169 Model and the VGG Model. In case of the DenseNet169 model, a model with the pre trained weights of ImageNet and one without it is experimented. Results obtained with these variants of CNN are comparedand it shows that DenseNet169 model that uses pre-trained weights of ImageNet model performs better than the other two models.
{"title":"Comparative Analysis of Deep Convolutional Neural Network Models for Humerus Bone Fracture Detection","authors":"A. Sasidhar, M. S. Thanabal","doi":"10.1166/jmihi.2021.3899","DOIUrl":"https://doi.org/10.1166/jmihi.2021.3899","url":null,"abstract":"Deep learning plays a key role in medical image processing. One of the applications of deep learning models in this domain is bone fracture detection from X-ray images. Convolutional neural network and its variants are used in wide range of medical image processing applications. MURA\u0000 Dataset is commonly used in various studies that detect bone fractures and this work also uses that dataset, in specific the Humerus bone radiograph images. The humerus dataset in the MURA dataset contains both images with fracture and without fracture. The image with fracture includes images\u0000 with metals which are removed in this work. Experimental analysis was made with two variants of convolutional neural network, DenseNet169 Model and the VGG Model. In case of the DenseNet169 model, a model with the pre trained weights of ImageNet and one without it is experimented. Results\u0000 obtained with these variants of CNN are comparedand it shows that DenseNet169 model that uses pre-trained weights of ImageNet model performs better than the other two models.","PeriodicalId":393031,"journal":{"name":"J. Medical Imaging Health Informatics","volume":"145 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":"124638507","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}
Brain Computer Interface (BCI) is a fast growing area of research to enable communication between our brains and computers. EEG based motor imagery BCI involves the user imagining movement, the subsequent recording and signal processing on the electroencephalogram signals from the brain, and the translation of those signals into specific commands. Ultimately, motor imagery BCI has the potential to be applied to helping those with special abilities recover motor control. This paper presents an evaluation of performance for EEG based motor imagery BCI with a classification accuracy of 80.2%, making use of features extracted using the Fast Fourier Transform and the Discrete Wavelet Transform, and classification is done using an Artificial Neural Network. It goes on to conclude how the performance is affected by the particular feature sets and neural network parameters.
{"title":"Signal Processing and Classification for Electroencephalography Based Motor Imagery Brain Computer Interface","authors":"A. Shankar, S. Muttan, D. Vaithiyanathan","doi":"10.1166/jmihi.2021.3904","DOIUrl":"https://doi.org/10.1166/jmihi.2021.3904","url":null,"abstract":"Brain Computer Interface (BCI) is a fast growing area of research to enable communication between our brains and computers. EEG based motor imagery BCI involves the user imagining movement, the subsequent recording and signal processing on the electroencephalogram signals from the brain,\u0000 and the translation of those signals into specific commands. Ultimately, motor imagery BCI has the potential to be applied to helping those with special abilities recover motor control. This paper presents an evaluation of performance for EEG based motor imagery BCI with a classification accuracy\u0000 of 80.2%, making use of features extracted using the Fast Fourier Transform and the Discrete Wavelet Transform, and classification is done using an Artificial Neural Network. It goes on to conclude how the performance is affected by the particular feature sets and neural network parameters.","PeriodicalId":393031,"journal":{"name":"J. Medical Imaging Health Informatics","volume":"45 23 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":"130223896","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 concept of wireless implantable medical devices (IMDs) is becoming more popular as the world’s population ages and concerns about public health grow. Implantable antennas have figured prominently in wireless communication among IMDs and external infrastructures, yet they have subsequently become a major study area. Among the most difficult aspects of building implantable antennas is to varied physical tissues and fluids act as dielectric stress on antenna, affecting its efficiency dramatically. Ground radiation antenna was particularly designed for the antenna size reduction. The features of the ground have an impact on it. There is variance in the radiation field with similar frequency and antenna length yet varied ground conductance. It has been discovered that when the ground conductance is low, the radiation field is minimal and the orientation of the radiation field modifies. A meandered-loop ground radiation antenna (MGRA) was designed by coupling the meandered-loop structure to the ground radiating plane using only one electrical element. The proposed antenna was studied for biomedical applications at ISM band in the range between 2.4 to 2.8 GHz. The overall size of antenna is 30×24 mm2 making it suitable for the implantable applications. The bandwidth of the MGRA was further improved by using stub structures. The single layer skin model simulation showed that |S11| parameter as −21.21 dB at the resounding frequency of 2.40 GHz. Major factors like impedance match gain, radiation effectiveness and Specific Absorption Rate (SAR) had also been evaluated in this study.
{"title":"Wide Band Meandered-Loop Ground Radiation Antenna for Biomedical Applications","authors":"R. Rajkumar, P. Marichamy","doi":"10.1166/jmihi.2021.3911","DOIUrl":"https://doi.org/10.1166/jmihi.2021.3911","url":null,"abstract":"The concept of wireless implantable medical devices (IMDs) is becoming more popular as the world’s population ages and concerns about public health grow. Implantable antennas have figured prominently in wireless communication among IMDs and external infrastructures, yet they have\u0000 subsequently become a major study area. Among the most difficult aspects of building implantable antennas is to varied physical tissues and fluids act as dielectric stress on antenna, affecting its efficiency dramatically. Ground radiation antenna was particularly designed for the antenna\u0000 size reduction. The features of the ground have an impact on it. There is variance in the radiation field with similar frequency and antenna length yet varied ground conductance. It has been discovered that when the ground conductance is low, the radiation field is minimal and the orientation\u0000 of the radiation field modifies. A meandered-loop ground radiation antenna (MGRA) was designed by coupling the meandered-loop structure to the ground radiating plane using only one electrical element. The proposed antenna was studied for biomedical applications at ISM band in the range between\u0000 2.4 to 2.8 GHz. The overall size of antenna is 30×24 mm2 making it suitable for the implantable applications. The bandwidth of the MGRA was further improved by using stub structures. The single layer skin model simulation showed that |S11| parameter as −21.21 dB at the\u0000 resounding frequency of 2.40 GHz. Major factors like impedance match gain, radiation effectiveness and Specific Absorption Rate (SAR) had also been evaluated in this study.","PeriodicalId":393031,"journal":{"name":"J. Medical Imaging Health Informatics","volume":"53 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":"133695169","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 most prevalent cancer that threatens women’s life is Breast cancer. According to WHO Statistics in 2020, 2.3 Million Women were diagnosed with Breast cancer and 685000 death rate were disclosed globally. In this paper, Wearable Health Diagnosis System (WHDS) based antenna for the identification of the early breast cancer is discussed. Conventional methods are limited by their uncomfortable testing setups, panic environment and failure in results. Recently, textile based antenna for microwave imaging stared to work on the detection of the cancer cells at the earlier stage in breast. WHDS antenna has the requirements of wider bandwidth, high resolution, low Specific Absorption Rate (SAR), bio compatibility, and flexibility. The proposed work is based on the textile antenna using Denim substrate (permittivity = 1.67, thickness = 2 mm) to diagnosis the Early Breast Cancer Tissues (EBCT). Using the following antenna parameters (return loss, E-filed, H-field and SAR values), the position and malignancy of the EBCT is identified. Since the dielectric properties of the cancer cells are high, the influence of the effective permittivity is higher on the E-field and SAR. Along with the above parameters, comparison of various substrate materials (Denim, FR4, and RT duroid) were also tested and Denim is selected for our application as it introduces greater reflection co-efficient and wider bandwidth. The proposed antenna is designed to operate at a frequency of 2–4 GHz. This miniaturised antenna has a volume of 30 × 28 × 2 mm3.
{"title":"Early Stage Breast Cancer Detection Using Wearable Health Diagnosis System","authors":"S. M. A. Banu, K. M. A. Jeyanthi","doi":"10.1166/jmihi.2021.3894","DOIUrl":"https://doi.org/10.1166/jmihi.2021.3894","url":null,"abstract":"The most prevalent cancer that threatens women’s life is Breast cancer. According to WHO Statistics in 2020, 2.3 Million Women were diagnosed with Breast cancer and 685000 death rate were disclosed globally. In this paper, Wearable Health Diagnosis System (WHDS) based antenna\u0000 for the identification of the early breast cancer is discussed. Conventional methods are limited by their uncomfortable testing setups, panic environment and failure in results. Recently, textile based antenna for microwave imaging stared to work on the detection of the cancer cells at the\u0000 earlier stage in breast. WHDS antenna has the requirements of wider bandwidth, high resolution, low Specific Absorption Rate (SAR), bio compatibility, and flexibility. The proposed work is based on the textile antenna using Denim substrate (permittivity = 1.67, thickness = 2 mm) to diagnosis\u0000 the Early Breast Cancer Tissues (EBCT). Using the following antenna parameters (return loss, E-filed, H-field and SAR values), the position and malignancy of the EBCT is identified. Since the dielectric properties of the cancer cells are high, the influence of the effective permittivity is\u0000 higher on the E-field and SAR. Along with the above parameters, comparison of various substrate materials (Denim, FR4, and RT duroid) were also tested and Denim is selected for our application as it introduces greater reflection co-efficient and wider bandwidth. The proposed antenna is designed\u0000 to operate at a frequency of 2–4 GHz. This miniaturised antenna has a volume of 30 × 28 × 2 mm3.","PeriodicalId":393031,"journal":{"name":"J. Medical Imaging Health Informatics","volume":"7 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":"122055083","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 medical data integrating system allows the hospital’s resource constraints to be more effectively utilized. Moreover, by improving the resource management and allocation method, the hospital’s operations may be more organized, and the effectiveness of healthcare can be improved without breaking the medical agreements. Significant catastrophes frequently result in a scarcity of important medical resources, hence resource allocation must be optimized to enhance the performance of relief operations. The two main requirements for healthcare industrial applications are timeliness and reliability. Therefore, in the architecture of a smart healthcare industry these two criteria should be thought carefully. A well-known approach for the security and timeliness in the intelligent healthcare industry is to utilize hybrid IoT and Cloud technologies. Yet it is not enough to protect their hard deadlines for tight time-sensitive applications utilizing cloud. A potential way to cope with efficiency and latency criteria for strict time-sensitive applications is the deployment of intermediate processing layer IoT that can be linked between healthcare industrial plant and cloud. The purpose of this article is to develop a healthcare Industrial IoT system that include a medical resource allocation scheme for dividing a certain amount of workload between those multiple computing layers which are dependable and time consuming. IOT is integration of microprocessors and controller Workload partitioning can give us important design decisions to specify how many computing resources are needed in cooperation with IoT to develop a local private cloud. Ant lion optimization (ALO) and TABU Look for the right route. The simplest method of deciding the distance to a destination is to choose an OLSR routing protocol depending on the meaning or measure it requires. The method proposed in the distribution and data storage of medical resources is very efficient.
{"title":"Reliability Aware Medical Resource Allocation for Health Care Industrial Internet of Things (IIoT) Using Tabu Search and Alo Algorithm","authors":"Ramesh Chandran, N. Gayathri, S. R. Kumar","doi":"10.1166/jmihi.2021.3908","DOIUrl":"https://doi.org/10.1166/jmihi.2021.3908","url":null,"abstract":"The medical data integrating system allows the hospital’s resource constraints to be more effectively utilized. Moreover, by improving the resource management and allocation method, the hospital’s operations may be more organized, and the effectiveness of healthcare can\u0000 be improved without breaking the medical agreements. Significant catastrophes frequently result in a scarcity of important medical resources, hence resource allocation must be optimized to enhance the performance of relief operations. The two main requirements for healthcare industrial applications\u0000 are timeliness and reliability. Therefore, in the architecture of a smart healthcare industry these two criteria should be thought carefully. A well-known approach for the security and timeliness in the intelligent healthcare industry is to utilize hybrid IoT and Cloud technologies. Yet it\u0000 is not enough to protect their hard deadlines for tight time-sensitive applications utilizing cloud. A potential way to cope with efficiency and latency criteria for strict time-sensitive applications is the deployment of intermediate processing layer IoT that can be linked between healthcare\u0000 industrial plant and cloud. The purpose of this article is to develop a healthcare Industrial IoT system that include a medical resource allocation scheme for dividing a certain amount of workload between those multiple computing layers which are dependable and time consuming. IOT is integration\u0000 of microprocessors and controller Workload partitioning can give us important design decisions to specify how many computing resources are needed in cooperation with IoT to develop a local private cloud. Ant lion optimization (ALO) and TABU Look for the right route. The simplest method of\u0000 deciding the distance to a destination is to choose an OLSR routing protocol depending on the meaning or measure it requires. The method proposed in the distribution and data storage of medical resources is very efficient.","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":"128512054","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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