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Brain Tumor:Hybrid Feature Extraction Based on UNet and 3dCNN

Computer Systems science & Engineering 2023年第5期45卷 2093-2109页

作者： Sureshkumar rajagopal Tamilvizhi Thanarajan Youseef Alotaibi Saleh Alghamdi Center for System design Chennai Institute of TechnologyChennai600069India department of Information technology Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India department of Computer science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia department of Information technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Automated segmentation of brain tumors using Magnetic resonance Imaging(MrI)data is critical in the analysis and monitoring of disease *** a result,gliomas are aggressive and diverse tumors that may be split into intra-tumoral groups by using effective and accurate segmentation *** is intended to extract characteristics from an image using the Gray Level Co-occurrence(GLC)matrix feature extraction method described in the proposed *** Convolutional Neural Networks(CNNs),which are commonly used in biomedical image segmentation,CNNs have significantly improved the precision of the state-of-the-art segmentation of a brain *** two segmentation networks,a U-Net and a 3d CNN,we present a major yet easy combinative technique that results in improved and more precise *** U-Net and 3d CNN are used together in this study to get better and more accurate estimates of what is going *** the dataset,two models were developed and assessed to provide segmentation maps that differed fundamentally in terms of the segmented tumour ***,the estimates was made by two separate models that were put together to produce the final *** comparison to current state-of-the-art designs,the precision(percentage)was 98.35,98.5,and 99.4 on the validation set for tumor core,enhanced tumor,and whole tumor,respectively.

关键词： Medical imaging segmentation U-net 3d CNN brain tumor

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dLMNN Based Heart disease Prediction with Pd-SS Optimization Algorithm

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Intelligent Automation & Soft Computing 2023年第2期35卷 1353-1368页

作者： S.raghavendra Vasudev Parvati r.Manjula Ashok Kumar Nanda ruby Singh d.Lakshmi S.velmurugan department of Information and Communication technology Manipal Institute of TechnologyManipal Academy of Higher EducationManipal576104India department of Information science and Engineering SDM College of Engineering and TechnologyDharwad580002India department of Computer science and Engineering School of ComputingSRM Institute of Science and TechnologyKattankulathurChennai603203India department of Computer science and Engineering B.V Raju Institute of TechnologyTelangana502313India Faculty of Engineering and technology SRM Institute of Science and TechnologyNCR CampusUttar Prasesh201204India School of Computing science and Engineering VIT Bhopal UniversityMadhya Pradesh466114India department of Computer science and Engineering Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India

In contemporary medicine,cardiovascular disease is a major public health *** diseases are one of the leading causes of death *** are classiﬁed as vascular,ischemic,or *** information contained in patients’Electronic Health records(EHr)enables clin-icians to identify and monitor heart *** failure rates have risen drama-tically in recent years as a result of changes in modern *** diseases are becoming more prevalent in today’s medical *** year,a substantial number of people die as a result of cardiac *** primary cause of these deaths is the improper use of pharmaceuticals without the supervision of a physician and the late detection of *** improve the efﬁciency of the classiﬁcation algo-rithms,we construct a data pre-processing stage using feature ***-ments using unidirectional and bidirectional neural network models found that a deep Learning Modiﬁed Neural Network(dLMNN)model combined with the Pet dog-Smell Sensing(Pd-SS)algorithm predicted the highest classiﬁcation performance on the UCI Machine Learning Heart disease *** dLMNN-based PdSS achieved an accuracy of 94.21%,an F-score of 92.38%,a recall of 94.62%,and a precision of 93.86%.These results are competitive and promising for a heart disease *** demonstrated that a dLMNN framework based on deep models may be used to solve the categorization problem for an unbalanced heart disease *** proposed approach can result in exceptionally accurate models that can be utilized to analyze and diagnose clinical real-world data.

关键词： Machine learning random forest coronary heart disease cardiovascular optimization algorithm

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Fuzzy Logic-Based Health Monitoring System for COVId’19 Patients

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Computers, Materials & Continua 2021年第5期67卷 2431-2447页

作者： M.Jayalakshmi Lalit Garg K.Maharajan K.Jayakumar Kathiravan Srinivasan Ali Kashif Bashir K.ramesh National Engineering College Kovilpatti628503India Faculty of Information and Communication technology University of MaltaMsidaMSD2080Malta vel tech rangarajan dr.sagunthala r&d institute of science and technology Chennai600062India School of Computer science and Engineering Vellore Institute of Technology(VIT)Vellore632014India School of Information technology and Engineering Vellore Institute of Technology(VIT)Vellore632014India department of Computing and Mathematics Manchester Metropolitan UniversityManchesterUK department of Computer science Karnataka State Akkamahadevi Women’s UniversityVijayapuraIndia

In several countries,the ageing population contour focuses on high healthcare costs and overloaded health care *** health care monitoring system can be a potential alternative,especially in the COVId-19 pandemic situation to help mitigate such problems by encouraging healthcare to transition from hospital-centred services to self-care,mobile care and home *** this aspect,we propose a pervasive system to monitor the COVId’19 patient’s conditions within the hospital and outside by monitoring their medical and psychological *** facilitates better healthcare assistance,especially for COVId’19 patients and quarantined *** identies the patient’s medical and psychological condition based on the current context and activities using a fuzzy context-aware reasoning engine based *** reasoning engine makes decisions using linguistic rules based on inference mechanisms that support the patient condition *** rules are framed based on the fuzzy set attributes belong to different context *** fuzzy semantic rules are used to identify the relationship among the attributes,and the reasoning engine is used to ensure precise real-time context interpretation and current evaluation of the *** are measured using a fuzzy logic-based context reasoning system under *** results indicate the usefulness of monitoring the COVId’19 patients based on the current context.

关键词： Pervasive healthcare COVId’19 context-aware fuzzy reasoning engine fuzzy logic role-based expert system

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