Abstract
Recently, vertebral tumor prediction using image-based vertebral bio-mathematical modeling with accurate segmentation has been considered as a significant area of research. Further, precise lumbar spinal segmentation is essential to the clinicians for tumor analysis. Therefore, precise and reliable segmentation process is required to help radiologists and doctors to identify different vertebrae tumors with better prediction ratio. The exact vertebral disks segmentation of spinal bones from medical images is a complex process in dealing with different deformities and pathologies in accordance to the conventional techniques such as Deep Convolutional Neural Network (DCNN), Finite Element Analysis (FEA), Principal Component and Factor Analysis (PCFA), Multi-Parameter Ensemble Learning (MPEL), Hierarchical Conditional Random Forest (HCRF), and Deep Siamese Neural Network (DSNN). Therefore, to overcome the present drawbacks, Analytical Transform Assisted Statistical Characteristic Decomposition Model (ATS-CDM) is proposed in this paper for the accurate prediction of vertebral tumor detection and segmentation. This technique is used for the calibration of the segmentation procedures in vertebral tumor image prediction and Receiver Operating Curve (ROC) grading for the lumbar spines. The significant objective of the model-fitting algorithm iterates the tumor regions and measures the current region's variance for the accurate identification of tumor. The outcomes show potential and promising results at lab scale evaluation through analyzing the vertebral datasets with Intra-Discal Pressure (IDP) images for experimental validation with 98.7% bending and 98.88 % segmentation accuracy leads to 94.2 ± 0.2 % to 97.02 ± 0.2 % average ROCgrading.
Highlights
Deep learning assisted multi-hidden layer neural networks can carry out complex task and have gained widespread interest of numerous medical fields, including radiology, pathology and tumor analysis [1]
During defining the vertebral shape, Fourier descriptors have been used to detect the tumor region in the spinal region which is considered as one of the prominent reasons for the back pain [20]. This description is included in the proposed Analytical Transform Assisted Statistical Characteristic Decomposition Model (ATS-CDM)
The intervertebral disk and ligament characteristics extracted by analytical transform assisted statistical characteristic decomposition model are better suited to the replication of an experiment for gradual reduction than ligament characteristics fitted to literature isolated ligament results with 0.408/0.408 resolution
Summary
Deep learning assisted multi-hidden layer neural networks can carry out complex task and have gained widespread interest of numerous medical fields, including radiology, pathology and tumor analysis [1]. During defining the vertebral shape, Fourier descriptors have been used to detect the tumor region in the spinal region which is considered as one of the prominent reasons for the back pain [20] This description is included in the proposed Analytical Transform Assisted Statistical Characteristic Decomposition Model (ATS-CDM). Vertebral tumor segmentation using ATS-CDM has been modelled with mathematical structure for feature learning, segmentation, and classification of vertebrae. In this analysis, the unmarked datasets for pre-training are used for the extraction of high-level features and for fine tuning the identified data.
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