Diabetic retinopathy is an eye disease in diabetic patients due to damage to the small blood vessels in the retina due to high and low blood sugar levels. Accurate detection and classification of Diabetic Retinopathy is an important task in computer-aided diagnosis, especially when planning for diabetic retinopathy surgery. Therefore, this study aims to design an automated model based on deep learning, which helps ophthalmologists detect and classify diabetic retinopathy severity through fundus images. In this work, a deep convolutional neural network (CNN) with transfer learning and fine tunes has been proposed by using pre-trained networks known as Residual Network-50 (ResNet-50). The overall framework of the proposed

Magnetic Resonance Imaging (MRI) uses magnetization and radio waves, rather than x-rays to make very detailed, cross- sectional pictures of the brain. In this work we are going to explain some procedures belongs contrast and brightness improvement which is very important in the improvement the image quality such as the manipulation with the image histogram. Its has been explained in this worked the histogram shrink i.e. reducing the size of the gray level gives a dim low contrast picture is produced, where, the histogram stretching of the gray level was distributed on a wide scale but there is no increase in the number of pixels in the bright region. The histogram equalization has also been discuss together with its effects of the improveme

Abstract The wavelet shrink estimator is an attractive technique when estimating the nonparametric regression functions, but it is very sensitive in the case of a correlation in errors. In this research, a polynomial model of low degree was used for the purpose of addressing the boundary problem in the wavelet reduction in addition to using flexible threshold values in the case of Correlation in errors as it deals with those transactions at each level separately, unlike the comprehensive threshold values that deal with all levels simultaneously, as (Visushrink) methods, (False Discovery Rate) method, (Improvement Thresholding) and (Sureshrink method), as the study was conducted on real monthly data represented in the rates of theft crimes f

BACKGROUND: The humeral shaft fractures have a good rate of union, despite this fact, still there is a significant rate of nonunion after nonoperative treatment and more often after operative treatment. AIM: The aim of the study is to evaluate the autogenous onlay graft with compression plate for treatment of persistent humeral shaft non-union with failed previous surgery both radiological and functional outcome. MATERIALS AND METHODS: A prospective study on twenty patients having persistent aseptic non-union age between 20 and 60 years old, after failed surgical treatment of fractures humeral shaft in Al-Zahra teaching and Al-Kindy teaching hospitals, while infected nonunion, diabetes mellitus, secondary metastasis, smoking, a

<p>In this paper, a simple color image compression system has been proposed using image signal decomposition. Where, the RGB image color band is converted to the less correlated YUV color model and the pixel value (magnitude) in each band is decomposed into 2-values; most and least significant. According to the importance of the most significant value (MSV) that influenced by any simply modification happened, an adaptive lossless image compression system is proposed using bit plane (BP) slicing, delta pulse code modulation (Delta PCM), adaptive quadtree (QT) partitioning followed by an adaptive shift encoder. On the other hand, a lossy compression system is introduced to handle the least significant value (LSV), it is based on

With its rapid spread, the coronavirus infection shocked the world and had a huge effect on billions of peoples' lives. The problem is to find a safe method to diagnose the infections with fewer casualties. It has been shown that X-Ray images are an important method for the identification, quantification, and monitoring of diseases. Deep learning algorithms can be utilized to help analyze potentially huge numbers of X-Ray examinations. This research conducted a retrospective multi-test analysis system to detect suspicious COVID-19 performance, and use of chest X-Ray features to assess the progress of the illness in each patient, resulting in a "corona score." where the results were satisfactory compared to the benchmarked techniques.  T

Compression is the reduction in size of data in order to save space or transmission time. For data transmission, compression can be performed on just the data content or on the entire transmission unit (including header data) depending on a number of factors. In this study, we considered the application of an audio compression method by using text coding where audio compression represented via convert audio file to text file for reducing the time to data transfer by communication channel. Approach: we proposed two coding methods are applied to optimizing the solution by using CFG. Results: we test our application by using 4-bit coding algorithm the results of this method show not satisfy then we proposed a new approach to compress audio fil