Novel Architecture of OneM2M-Based Convergence Platform for Mixed Reality and IoT

Sikkandar, Mohamed Yacin; Begum, S. Sabarunisha; Alkathiry, Abdulaziz A.; Alotaibi, Mashhor Shlwan N.; Manzar, Dilshad

doi:10.32604/cmc.2022.019635

Cited by 2 publications

(1 citation statement)

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“…Gray Level Co-occurrence Matrix (GLCM) which is also termed as Gray -Level Spatial Dependence Matrix counts the total number of times that particular value of pairs of pixel happen to be in an image in a particular spatial relation [26]. Generally, the spatial relation is the required pixel and the next pixel just to its immediate right horizontally.…”

Section: Glcm For Feature Extractionmentioning

confidence: 99%

Expeditious detection and segmentation of bone mass variation in DEXA images using the hybrid GLCM-AlexNet approach

Amiya,

Murugan,

Ramaraj

et al. 2023

Preprint

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Osteoporosis (OP) is an osteometabolic disorder characterized by a lesser bone mineral density (BMD) and the disruption of bone tissue micro - architecture, resulting in a greater bone fragility and higher likelihoods of fractures. OP emerges once the bone mass declines faster than the body's capacity to replenish it, leading to a significant reduction in the strength of bone. OP impacts each and every bone throughout the body and provides no clinical signs until a fracture happens. Aging leads to the reduction in BMD, and the rate of percentage of fractures rises over time, usually causing mortality and morbidity. Numerous BMD evaluation methods are available, and they are used in a variety of settings by considering the location of the fracture. Dual-energy x-ray absorptiometry (DEXA/DXA) is recognized as the gold standard for predicting the fracture, since it is the most advanced, technologically affirmed, and also has excellent performance. According to the findings, most researchers do not endeavour the identification and the segmentation of low bone masses from DEXA images. Medical image segmentation supports in analyzing and visualizing the bone's low bone mass. The envisaged hybrid approach, that integrates GLCM for feature extraction and AlexNet for a low bone mass variation classification, provides segmented images that assist in categorizing bone health as normal, osteopenia, or osteoporosis. The developed algorithm's performance metrics, including Dice Co-efficient, Sensitivity, and Specificity, were 92.35%, 90.26%, and 92.42%, respectively. The Orthopedicians ascertained the efficacy of the outcomes rendered by the proposed algorithm.

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Section: Glcm For Feature Extractionmentioning

confidence: 99%