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Disease Diagnosis System Using IoT Empowered with Fuzzy Inference System

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Computers, Materials & Continua 2022年第3期70卷 5305-5319页

作者： Talha Mahboob Alam Kamran Shaukat Adel Khelifi Wasim Ahmad Khan Hafiz Muhammad Ehtisham Raza Muhammad Idrees suhuai luo Ibrahim A.Hameed Department of Computer Science and Information Technology Virtual University of PakistanLahorePakistan School of Information and Physical Sciences The University of NewcastleAustralia Department of Computer Science and Information Technology Abu Dhabi UniversityAbu DhabiUnited Arab Emirates School of Computer Science National College of Business Administration&EconomicsLahorePakistan Sir Syed College of Computer Science University of Engineering and TechnologyLahorePakistan Department of Data Science University of the PunjabLahorePakistan Department of ICT and Natural Sciences Norwegian University of Science and TechnologyTrondheimNorway

Disease diagnosis is a challenging task due to a large number of associated *** in the diagnosis process arises frominaccuracy in patient attributes,missing data,and limitation in the medical expert’s ability to define cause and effect relationships when there are multiple interrelated *** paper aims to demonstrate an integrated view of deploying smart disease diagnosis using the Internet of Things(IoT)empowered by the fuzzy inference system(FIS)to diagnose various *** Fuzzy Systemis one of the best systems to diagnose medical conditions because every disease diagnosis involves many uncertainties,and fuzzy logic is the best way to handle *** proposed system differentiates new cases provided symptoms of the ***,it becomes a time-sensitive task to discriminate symptomatic *** proposed system can track symptoms firmly to diagnose diseases through IoT and FIS smartly and *** coefficients have been employed to predict and compute the identified disease’s severity for each sign of *** study aims to differentiate and diagnose COVID-19,Typhoid,Malaria,and *** study used the FIS method to figure out the disease over the use of given data related to correlating with input *** tool is utilised for the implementation of *** procedure on the aforementioned given data presents that affectionate disease can derive from the *** results of our proposed method proved that FIS could be utilised for the diagnosis of other *** study may assist doctors,patients,medical practitioners,and other healthcare professionals in early diagnosis and better treat diseases.

关键词： Disease diagnosis system COVID-19 healthcare biomedical rules extraction

Liver Segmentation from CT Image Using Fuzzy Clustering and Level Set

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Journal of Signal and Information Processing 2013年第3期4卷 36-42页

作者： Xuechen Li suhuai luo Jiaming Li The CSIRO ICT Centre Australia The University of Newcastle Australia

This paper presents a fully automatic segmentation method of liver CT scans using fuzzy c-mean clustering and level set. First, the contrast of original image is enhanced to make boundaries clearer;second, a spatial fuzzy c-mean clustering combining with anatomical prior knowledge is employed to extract liver region automatically;thirdly, a distance regularized level set is used for refinement;finally, morphological operations are used as post-processing. The experiment result shows that the method can achieve high accuracy (0.9986) and specificity (0.9989). Comparing with standard level set method, our method is more effective in dealing with over-segmentation problem.

关键词： Liver Segmentation Fuzzy c-Mean Clustering Level Set

Automatic Alzheimer’s Disease Recognition from MRI Data Using Deep Learning Method

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Journal of Applied Mathematics and Physics 2017年第9期5卷 1892-1898页

作者： suhuai luo Xuechen Li Jiaming Li School of Electrical Engineering and Computing The University of Newcastle Callaghan Australia School of Computer Science Shenzhen University Shenzhen China Quantitative Imaging Research Team CSIRO Data61 Marsfield Australia

Alzheimer’s Disease (AD), the most common form of dementia, is an incurable neurological condition that results in a progressive mental deterioration. Although definitive diagnosis of AD is difficult, in practice, AD diagnosis is largely based on clinical history and neuropsychological data including magnetic resource imaging (MRI). Increasing research has been reported on applying machine learning to AD recognition in recent years. This paper presents our latest contribution to the advance. It describes an automatic AD recognition algorithm that is based on deep learning on 3D brain MRI. The algorithm uses a convolutional neural network (CNN) to fulfil AD recognition. It is unique in that the three dimensional topology of brain is considered as a whole in AD recognition, resulting in an accurate recognition. The CNN used in this study consists of three consecutive groups of processing layers, two fully connected layers and a classification layer. In the structure, every one of the three groups is made up of three layers, including a convolutional layer, a pooling layer and a normalization layer. The algorithm was trained and tested using the MRI data from Alzheimer’s Disease Neuroimaging Initiative. The data used include the MRI scanning of about 47 AD patients and 34 normal controls. The experiment had shown that the proposed algorithm delivered a high AD recognition accuracy with a sensitivity of 1 and a specificity of 0.93.

关键词： Alzheimer’s Disease AD Recognition Magnetic Resource Imaging MRI Deep Learning Convolutional Neural Network CNN

Review on the Methods of Automatic Liver Segmentation from Abdominal Images

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Journal of Computer and Communications 2014年第2期2卷 1-7页

作者： suhuai luo Xuechen Li Jiaming Li The CSIRO Computational Informatics Australia The University of Newcastle Australia

Automatic liver segmentation from abdominal images is challenging on the aspects of segmentation accuracy, automation and robustness. There exist many methods of liver segmentation and ways of categorisingthem. In this paper, we present a new way of summarizing the latest achievements in automatic liver segmentation. We categorise a segmentation method according to the image feature it works on, therefore better summarising the performance of each category and leading to finding an optimal solution for a particular segmentation task. All the methods of liver segmentation are categorized into three main classes including gray level based method, structure based method and texture based method. In each class, the latest advance is reviewed with summary comments on the advantages and drawbacks of each discussed approach. Performance comparisons among the classes are given along with the remarks on the problems existed and possible solutions. In conclusion, we point out that liver segmentation is still an open issue and the tendency is that multiple methods will be employed together to achieve better segmentation performance.

关键词： Liver Segmentation Image Feature Performance Comparison Review Survey

Automatic and Robust Segmentation of Multiple Sclerosis Lesions with Convolutional Neural Networks

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Computers, Materials & Continua 2021年第1期66卷 977-991页

作者： H.M.Rehan Afzal suhuai luo Saadallah Ramadan Jeannette Lechner-Scott Mohammad Ruhul Amin Jiaming Li M.Kamran Afzal M.Kamran Afzal School of Electrical Engineering and Computing University of NewcastleCallaghanNSW 2308Australia Hunter Medical Research Institute New Lambton HeightsNSW 2305Australia Department of Neurology John Hunter HospitalNew Lambton HeightsNSW 2305Australia CSIRO Data61 MarsfieldNSW 2122Australia School of Computer Science and Technology Xiamen UniversityXiamen361005China

The diagnosis of multiple sclerosis(MS)is based on accurate detection of lesions on magnetic resonance imaging(MRI)which also provides ongoing essential information about the progression and status of the *** detection of lesions is very time consuming and lacks *** of the lesions are difficult to detect manually,especially within the grey *** paper proposes a novel and fully automated convolution neural network(CNN)approach to segment *** proposed system consists of two 2D patchwise CNNs which can segment lesions more accurately and *** first CNN network is implemented to segment lesions accurately,and the second network aims to reduce the false positives to increase *** system consists of two parallel convolutional pathways,where one pathway is concatenated to the second and at the end,the fully connected layer is replaced with *** routine MRI sequences T1-w,T2-w and FLAIR are used as input to the CNN,where FLAIR is used for segmentation because most lesions on MRI appear as bright regions and T1-w&T2-w are used to reduce MRI *** evaluated the proposed system on two challenge datasets that are publicly available from MICCAI and *** and qualitative evaluation has been performed with various metrics like false positive rate(FPR),true positive rate(TPR)and dice similarities,and were compared to current state-of-the-art *** proposed method shows consistent higher precision and sensitivity than other *** proposed method can accurately and robustly segment MS lesions from images produced by different MRI scanners,with a precision up to 90%.

关键词： Multiple sclerosis lesion segmentation automatic segmentation CNN automated tool lesion detection

Improvement of Liver Segmentation by Combining High Order Statistical Texture Features with Anatomical Structural Features

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Engineering（科研） 2013年第5期5卷 67-72页

作者： suhuai luo Xuechen Li Jiaming Li The CSIRO ICT Centre Australia The University of Newcastle Australia

Automatic segmentation of liver in medical images is challenging on the aspects of accuracy, automation and robustness. A crucial stage of the liver segmentation is the selection of the image features for the segmentation. This paper presents an accurate liver segmentation algorithm. The approach starts with a texture analysis which results in an optimal set of texture features including high order statistical texture features and anatomical structural features. Then, it creates liver distribution image by classifying the original image pixelwisely using support vector machines. Lastly, it uses a group of morphological operations to locate the liver organ accurately in the image. The novelty of the approach is resided in the fact that the features are so selected that both local and global texture distributions are considered, which is important in liver organ segmentation where neighbouring tissues and organs have similar greyscale distributions. Experiment results of liver segmentation on CT images using the proposed method are presented with performance validation and discussion.

关键词： Liver Segmentation Texture Feature Support Vector machine Morphological Operation

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用于单路数字式报纸传真机的预测编码

南京邮电学院学报 1988年第4期 35-42页

作者：毕厚杰罗苏淮祁红慧南京邮电学院无线电工程系

本文根据中文报纸传真的信源统计特性,提出用二维预测一维游程的修正Huffman编码,对数字式报纸传真进行信息保持型信源编码。本文介绍了其编码原理、最佳预测结构的选取以及部分实验结果。对中文文字占大部分的报纸样张,这种方法的实际... 详细信息

本文根据中文报纸传真的信源统计特性,提出用二维预测一维游程的修正Huffman编码,对数字式报纸传真进行信息保持型信源编码。本文介绍了其编码原理、最佳预测结构的选取以及部分实验结果。对中文文字占大部分的报纸样张,这种方法的实际编码压缩比达10倍以上。

关键词：编码预测技术哈夫曼编码报纸传真机

Semiconductor Materials Genome Initiative

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Journal of Semiconductors 2019年第1期 106-106页

作者： suhuai Wei Junwei luo Bing Huang Beijing Computational Science Research Center Institute of Semiconductors CAS

J. Semicond. 2018, Vol. 39, Issue 7Advanced semiconducting materials have many applications in information technology and clean energy, thus they are critical to sustained economic and environmental development. In recent years, great progresses have been made in theory and computational algorithms to discover and design new functional materials using materials genome techniques and

关键词： Semiconductor Materials Genome Initiative

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国内LNG运输技术与设备的发展现状

低温与特气 2016年第2期34卷 11-14页

作者：郭旭罗晓钟王荣华王苏淮徐周凯王芳云苗娜张家港中集圣达因低温装备有限公司江苏张家港215600

作为LNG工业体系中的物流环节,LNG运输业承上启下,贯穿整个LNG工业体系的方方面面,并随着LNG的需求和供应而迅速发展。文章简要介绍了LNG在我国的发展历程,对我国LNG陆路运输、海上运输和管道运输目前所处的状态和未来的发展方向做了扼... 详细信息

作为LNG工业体系中的物流环节,LNG运输业承上启下,贯穿整个LNG工业体系的方方面面,并随着LNG的需求和供应而迅速发展。文章简要介绍了LNG在我国的发展历程,对我国LNG陆路运输、海上运输和管道运输目前所处的状态和未来的发展方向做了扼要的分析,并对LNG运输业的发展提出了建议和意见。

关键词： LNG 槽车罐箱管道船用运输罐发展现状

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