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 anddiverse tumors that may be split into intr...
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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.
In contemporary medicine,cardiovasculardisease 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 ...
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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.
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 situa...
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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.
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