Machine Learning-Based Channel Analysis for User Concentric Optical Switching Networks

AlZubi, Ahmad Ali; Alarifi, Abdulaziz; Alnumay, Waleed S.

doi:10.1007/s00034-019-01165-3

Cited by 1 publication

(1 citation statement)

References 26 publications

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“…Some of the automated and semiautomated approaches discussed above are highlighted with the main strengths and drawbacks. However, the growing rate of data and increasing rate of computational power make the machine learning and deep learning approaches most promising for future applications concerning IoT applications [79][80][81][82][83].…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Techniques for Quantification of Knee Segmentation from MRI

Singla

Abugabah

et al. 2020

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Magnetic resonance imaging (MRI) is precise and efficient for interpreting the soft and hard tissues. Moreover, for the detailed diagnosis of varied diseases such as knee rheumatoid arthritis (RA), segmentation of the knee magnetic resonance image is a challenging and complex task that has been explored broadly. However, the accuracy and reproducibility of segmentation approaches may require prior extraction of tissues from MR images. The advances in computational methods for segmentation are reliant on several parameters such as the complexity of the tissue, quality, and acquisition process involved. This review paper focuses and briefly describes the challenges faced by segmentation techniques from magnetic resonance images followed by an overview of diverse categories of segmentation approaches. The review paper also focuses on automatic approaches and semiautomatic approaches which are extensively used with performance metrics and sufficient achievement for clinical trial assistance. Furthermore, the results of different approaches related to MR sequences used to image the knee tissues and future aspects of the segmentation are discussed.

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