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.

最近，治疗中转移性脊柱肿瘤的预测评估被认为是重要的研究领域。此外，对于脊柱临床诊断，经常使用来自不同模态的大量图像数据，并且基于自动椎骨识别可互换地分析这些数据。它包括椎骨位置的识别和几种图像形式的识别。由于MR或CT图像的外观或椎骨的形状/大小的差异，然而，在当前的常规医学研究中难以识别。MRI手动执行的椎骨肿瘤分割是传统研究算法的一项重要且耗时的过程。鉴定脊柱肿瘤的大小和位置的准确性在有效的肿瘤诊断和治疗中起着重要作用。因此，本文提出了一种分层隐马尔可夫随机场模型（HHMRF）来预测椎骨肿瘤，以便有效，有效地进行早期发现和诊断。这项研究的重要性是要在医疗物联网（IoMT）上的MRI图像中使用HHMRF和阈值技术实施最新的肿瘤检测策略。HHMRF可以协调脊椎肿瘤组织均匀区域的最后部分，同时使用数学计算更有效地保留不同组织成分之间的边缘。所提出的方法在使用深度神经网络对腰椎管狭窄症进行诊断和分割方面取得了最先进的性能，并以97.44％的准确度和97进行了实验分析。