Background: Differentiation between malignant and benign vertebral compression fracture is often problematic. This is precisely difficult in elderly who are predisposed to benign compression caused by osteoporosis .Establishing correct diagnosis is of great importance in determining the treatment andprognosis.A study was performed to determine which magnetic resonance imaging findings are useful in discrimination between metastatic and acute osteoporotic compression fractures of the spine. Recently MRI is being increasingly used for evaluation of these fractures.Objectives: The aim of this study is to establish the correct diagnosis of malignant and benign compression vertebral fracture by MRI to determine treatment and prognosis.Methods: MRI of (45 )patients with vertebral compression fracture who underwent MRI of spine during 2 years period ( 2004-2006) in Neuro surgical hospital were retrospectively reviewed by an expedient radiologist , A collapsed vertebra were considered to be acute if there was recent history of back pain of less than 3moths. All MRI sequences needed for spine examination was done( T1WI SE, T2WI GE STIR, and Mylography ) and Gadolinium contrast medium was given to all patients..Results: Of total 45 patients (31male, 14 female, their age range between 18-72years) , The criteria used to differentiate between benign andmalignant collapse spine on MRI was based on the signal intensity, morphology and contrast enhancement for the correct diagnosis. Homogenous and diffuse abnormal signal intensity, posterior convexity and involvement of pedicles are sign that are strongly suggestive of malignant collapse .Conversely, a band like area of low signal intensity adjacent to depressed end plateand preservation of signal intensity of vertebra suggest benign nature of the collapse.Conclusions: We found that MRI features is helpful in differentiation of malignant and benign compression fractures in majority of cases, and if initial MRI finding are equivocal correlation with other images technique, follow up and biopsy in selected cases helpful in arriving correct diagnosis
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The issue of image captioning, which comprises automatic text generation to understand an image’s visual information, has become feasible with the developments in object recognition and image classification. Deep learning has received much interest from the scientific community and can be very useful in real-world applications. The proposed image captioning approach involves the use of Convolution Neural Network (CNN) pre-trained models combined with Long Short Term Memory (LSTM) to generate image captions. The process includes two stages. The first stage entails training the CNN-LSTM models using baseline hyper-parameters and the second stage encompasses training CNN-LSTM models by optimizing and adjusting the hyper-parameters of
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Variable selection is an essential and necessary task in the statistical modeling field. Several studies have triedto develop and standardize the process of variable selection, but it isdifficultto do so. The first question a researcher needs to ask himself/herself what are the most significant variables that should be used to describe a given dataset’s response. In thispaper, a new method for variable selection using Gibbs sampler techniqueshas beendeveloped.First, the model is defined, and the posterior distributions for all the parameters are derived.The new variable selection methodis tested usingfour simulation datasets. The new approachiscompared with some existingtechniques: Ordinary Least Squared (OLS), Least Absolute Shrinkage
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Objectives: The purpose of the study is to ascertain the relationship between the training program and the socio-demographic features of patients with peptic ulcers in order to assess the efficiency of the program on patients' nutritional habits.
Methodology: Between January 17 and October 30 of 2022, The Center of Gastrointestinal Medicine and Surgery at Al-Diwanyiah Teaching Hospital conducted "a quasi-experimental study". A non-probability sample of 30 patients for the case group and 30 patients for the control group was selected based on the study's criteria. The study instrument was divided into 4 sections: the first portion contained 7 questions about demographic information, the second sect
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