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Vertebral tumors have a percentage of back pain that causes other vertebral region-born symptoms. Cancers that affect the vertebral column are visceral organ cancer metastases that are mostly seen in older patients. Vertebral dysfunction and neurological failure vertebral column cancers are the most important occurred cancers for patients. In the past, only few methods have been used to combat main and metastatic vertebral tumors. These methods are accessible for short-term monitoring and possess standardized classification consistency for vertebral diagnosis. In this paper, geometric rough propagation neural network has been used for the identification of genetic factors in the examination of a clinical sample with vertebral columns. The proposed neural network has C-statistics of 79.1%, a parameter pitch of 96.1%, and configuration for measurement in the study range with the Brier's score of 95.6%. The algorithm shows great net gain on the decision curve study, with promising performance results of 98.5% on internal testing for preoperative non-routine estimation of discharges with 0.5% error rate and 96% accuracy range. Also, these models have been externally validated by the online healthcare careers cloud-based open access web application on Internet of Medical Things Platform with 97.9% specificity ratio. [ABSTRACT FROM AUTHOR]

Thrombosis is one of the leading causes of death worldwide. Out of four, one person is dying of thrombosis; yet, the seriousness of this disease is underappreciated. Its early prediction and prevention continue to be a dilemma that confuses researchers. Nevertheless, a light can be seen at the end of the tunnel; thanks to nanoscience which has led to the development of new generations of nanostructure with different applications in bio-medicine and bio-engineering. The key paradigm for the Internet of Nano Things (IoNT) has allowed for new medical data to be collected which potentially helps achieve more accurate disease prediction. It has enabled real-time health services and turned the physical space of a patient into a smart space. While an enabler for several applications, the artificial nature of Internet of Nano Things devices can be harmful where the implementation of Nano Things may lead to unintended health effects. To overcome this issue, researchers have suggested the novel paradigm of the IoBNT that combines nanotechnology with tools from synthetic biology to provide reengineering of biological embedded computing devices. IoBNT promises many medical applications, such as intra-body sensing and actuation networks, based on biological cells and their characteristics in the biochemical field. In this paper, a novel IoBNT-based model with an optimized Bio-Cyber communication interface that helps predict and analyze blood vessel clots is introduced. The model utilizes a bio-interface to collect information on the blood vessels and convert it into an electrical equivalent format. Furthermore, the optical or thermal responsiveness excites the release of definite nano-carrier molecules such as liposomes which may be devised across the bloodstream and enter the targeted area passively to stimulate suitable nano-devices to predict the clots. The Bio-Cyber interface is used for linking the traditional electromagnetic wave to the Bio-Signaling Network based on the bioluminescence concept. Lab-scale simulation analysis shows prominent outcomes in the prediction of blood vessel clots with 97.66% accuracy and 12.22% tolerance level in error rate. [ABSTRACT FROM AUTHOR]

According to the survey on various health centres, smart log-based multi access physical monitoring system determines the health conditions of humans and their associated problems present in their lifestyle. At present, deficiency in significant nutrients leads to deterioration of organs, which creates various health problems, particularly for infants, children, and adults. Due to the importance of a multi access physical monitoring system, children and adolescents' physical activities should be continuously monitored for eliminating difficulties in their life using a smart environment system. Nowadays, in real-time necessity on multi access physical monitoring systems, information requirements and the effective diagnosis of health condition is the challenging task in practice. In this research, wearable smart-log patch with Internet of Things (IoT) sensors has been designed and developed with multimedia technology. Further, the data computation in that smart-log patch has been analysed using edge computing on Bayesian deep learning network (EC-BDLN), which helps to infer and identify various physical data collected from the humans in an accurate manner to monitor their physical activities. Then, the efficiency of this wearable IoT system with multimedia technology is evaluated using experimental results and discussed in terms of accuracy, efficiency, mean residual error, delay, and less energy consumption. This state-of-the-art smart-log patch is considered as one of evolutionary research in health checking of multi access physical monitoring systems with multimedia technology. [ABSTRACT FROM AUTHOR]

• This paper presents the HHMRF to predict the vertebral tumor for the early detection. • The importance of this research is to implement a strategy for detection of tumors. • It has been used to implement the threshold techniques in MRI images on the IoMT. • This method attains the better performance on segmentation of lumbar spinal stenosis. Recently, prediction evaluation of the metastatic spine tumors in therapy is considered a significant area of research. Further, for spinal clinical diagnoses, a large amount of image data from different modalities is often used and interchangeably analyzed based on the automatic vertebra identification. It includes recognition of vertebral positions and recognition in several image modalities. Due to the differences in MR or CT images appearance or shape/size of the vertebras, the identification is however difficult in the present conventional medical research. The segmentation of vertebral tumors that are manually performed by MRI is an important and time-consuming process by the conventional research algorithms. The accuracy of identification of the size and location of spine tumors plays a major role in effective tumor diagnosis and treatment. Therefore, this paper presents the Hierarchical Hidden Markov Random Field Model (HHMRF) to predict the vertebral tumor for the early detection and diagnosis treatment in an effective and efficient manner. The importance of this research is to implement a state-of-the-art strategy for detection of tumors using HHMRF and threshold techniques in MRI images on the Internet of Medical Things Platform (IoMT). HHMRF can coordinate the final section of vertebral tumor homogeneous areas of tissue while preserving the edges between different tissue constituents more effectively using mathematical computation. The proposed method attains the state-of-the-art performance on the diagnosis and segmentation of lumbar spinal stenosis using deep neural network and experimentally analyzed with 97.44% accuracy and 97.11% efficiency ratio on IoMT platform whereas proposed HHMRF achieves 98.5% high precision ratio compared to other existing TDCN (78.2%), DLA (81.6%), M-CNN (78.9%), and DCE-MRI (80.2%) methods. [ABSTRACT FROM AUTHOR]