The meniscus has a crucial function in human anatomy, and Magnetic Resonance Imaging (M.R.I.) plays an essential role in meniscus assessment. It is difficult to identify cartilage lesions using typical image processing approaches because the M.R.I. data is so diverse. An M.R.I. data sequence comprises numerous images, and the attributes area we are searching for may differ from each image in the series. Therefore, feature extraction gets more complicated, hence specifically, traditional image processing becomes very complex. In traditional image processing, a human tells a computer what should be there, but a deep learning (D.L.) algorithm extracts the features of what is already there automatically. The surface changes become valuable when diagnosing a tissue sample. Small, unnoticeable changes in pixel density may indicate the beginning of cancer or tear tissue in the early stages. These details even expert pathologists might miss. Artificial intelligence (A.I.) and D.L. revolutionized radiology by enhancing efficiency and accuracy of both interpretative and non-interpretive jobs. When you look at AI applications, you should think about how they might work. Convolutional Neural Network (C.N.N.) is a part of D.L. that can be used to diagnose knee problems. There are existing algorithms that can detect and categorize cartilage lesions, meniscus tears on M.R.I., offer an automated quantitative evaluation of healing, and forecast who is most likely to have recurring meniscus tears based on radiographs.
Background A prospective clinical study was
performed to compare the efficacy of the use of lowmolecular-
weight heparin group (enoxparin group)
with control group in the prevention of deep-vein
thrombosis after total knee arthroplasty.
Aim of the study: to assess the prevalence of DVT
after total knee arthroplasty and evaluate the
importance of the use of low molecular weight
heparin in the prevention of this DVT.
Methods Thirty-three patients undergoing total
knee arthroplasty were randomly divided into two
groups. One group consisted of 12 patients who
received no prophylaxis with an anticoagulant (the
control group), other group consisted of 21 patients
who received the low-molecular-weight h
CdO films were deposited on substrates from glass, Silicon and Porous silicon by thermal chemical spray pyrolysis technique with different thicknesses (130 and 438.46) nm. Measurements of X-ray diffraction of CdO thin film proved that the structure of the Polycrystalline is cubic lattice, and its crystallite size is located within nano scale range where the perfect orientation is (200). The results show that the surface’s roughness and the root mean square increased with increasing the thickness of prepared films. The UV-Visible measurements show that the CdO films with different thicknesses possess an allowed direct transition with band gap (4) eV. AFM measurement revealed that the silicon porosity located in nano range. Cadmium oxide f
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Two unsupervised classifiers for optimum multithreshold are presented; fast Otsu and k-means. The unparametric methods produce an efficient procedure to separate the regions (classes) by select optimum levels, either on the gray levels of image histogram (as Otsu classifier), or on the gray levels of image intensities(as k-mean classifier), which are represent threshold values of the classes. In order to compare between the experimental results of these classifiers, the computation time is recorded and the needed iterations for k-means classifier to converge with optimum classes centers. The variation in the recorded computation time for k-means classifier is discussed.
Always MRI and CT Medical images are noisy so that preprocessing is necessary for enhance these images to assist clinicians and make accurate diagnosis. Firstly, in the proposed method uses two denoising filters (Median and Slantlet) are applied to images in parallel and the best enhanced image gained from both filters is voted by use PSNR and MSE as image quality measurements. Next, extraction of brain tumor from cleaned images is done by segmentation method based on k-mean. The result shows that the proposed method is giving an optimal solution due to denoising method which is based on multiple filter types to obtain best clear images and that is leads to make the extraction of tumor more precision best.<
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In this review, numerous analytical methods to distinguish pigments in tattoo, paint, and ink items are discussed. The selection of a method was dependent upon the purpose, e.g., quantification or identification of pigments. The introductory part of this review focuses on describing the importance of setting up a pigment-associated safety profile. The formation of different degradation chemical substances as well as impurity trends can be indicated through the chemical investigation of pigments in tattoo products. It is noteworthy that pigment recognition in tattoo inks can work as a preliminary method to identify the pigments in a patient's tattoo before being removed by laser therapy. Contrary to the stud
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In this review, numerous analytical methods to distinguish pigments in tattoo, paint, and ink items are discussed. The selection of a method was dependent upon the purpose, e.g., quantification or identification of pigments. The introductory part of this review focuses on describing the importance of setting up a pigment-associated safety profile. The formation of different degradation chemical substances as well as impurity trends can be indicated through the chemical investigation of pigments in tattoo products. It is noteworthy that pigment recognition in tattoo inks can work as a preliminary method to identify the pigments in a patient's tattoo before being removed by laser therapy. Contrary to the stud
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Tested effective Alttafaria some materials used for different purposes, system a bacterial mutagenesis component of three bacterial isolates belonging to different races and materials tested included drug Briaktin
Dropping packets with a linear function between two configured queue thresholds in Random Early Detection (RED) model is incapable of yielding satisfactory network performance. In this article, a new enhanced and effective active queue management algorithm, termed Double Function RED (DFRED in short) is developed to further curtail network delay. Specifically, DFRED algorithm amends the packet dropping probability approach of RED by dividing it into two sub-segments. The first and second partitions utilizes and implements a quadratic and linear increase respectively in the packet dropping probability computation to distinguish between two traffic loads: low and high. The ns-3 simulation performance evaluations clearly indicate t
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