Today world thinks about coronavirus disease that which means all even this pandemic disease is not unique. The purpose of this study is to detect the role of machine-learning applications and algorithms in investigating and various purposes that deals with COVID-19. Review of the studies that had been published during 2020 and were related to this topic by seeking in Science Direct, Springer, Hindawi, and MDPI using COVID-19, machine learning, supervised learning, and unsupervised learning as keywords. The total articles obtained were 16,306 overall but after limitation; only 14 researches of these articles were included in this study. Our findings show that machine learning can produce an important role in COVID-19 investigations, prediction, and discrimination. In conclusion, machine learning can be involved in the health provider programs and plans to assess and triage the COVID-19 cases. Supervised learning showed better results than other Unsupervised learning algorithms by having 92.9% testing accuracy. In the future recurrent supervised learning can be utilized for superior accuracy.

This study highlights the role of imaging modalities through prostate image segmentation, using various algorithms depending on segmentation accuracy, and (VIoT) impact on improving imaging, via analyzing relevant articles to prostate segmentation during 2018–2021, from Springer, Science Direct, MDPI, IEEE, Nature Portfolio, Hindawi, with Taylor and Francis pre and during COVID-19. This study deals with 20 articles. Findings illustrated MRI was involved in (90%) of the articles in pre-COVID-19, while during COVID-19 declined to (60%). Furthermore, CNN algorithm was the most dependent method for prostate segmentation which was (50%) of the articles rather than other models. Whereas (80%) of the articles were depended on (DSC). In conclusion, the (VIoT) shows a significant role in all imaging modalities specifically MRI due to the real-time imaging. COVID-19 had impact on prostate segmentation research with the publishers was considerable in pre and during the pandemic. In addition, the best-utilized imaging modality was MRI due to its high image quality and ease applicable for (VIoT). Nevertheless, there is no study considered transabdominal ultrasound database as imaging modality for prostate segmentation. On the other hand, the segmentation performance referred to (DSC) that has a significant influence on prostate image segmentation Quality and performance.

Pelviureteric junction (PUJ) obstruction is a condition frequently encountered in both adult and pediatric patients. Congenital abnormalities and crossing lower-pole renal vessels are the most common underlying pathologies in both men and women. There are different methods for detecting it the most usual, safe, and easy one is by ultrasound scanning. The aim of this study is how to improve the image quality of two dimensional (2D) ultrasound screening of detecting PUJ dilatation using image processing software, image enhancement, and different types of filters, then making comparison which filter is the best to improve the image quality, that helps the medical doctors, and sonographers to make the correct decision and diagnosis. 1357 patients scanned by ultrasound in Harer general hospital for general abdominal scanning, 987 cases among them have detected as urinary tract infection cases among this 987 case there were 73 case of them with PUJ dilatation. The 2D ultrasound images saved, after making image enhancement and using different types of filters (HE, ADH, CLAHE, and Wiener) to enhance four 2D ultrasound images of abnormal kidneys, the result was in each type of filters there were some advantages and disadvantages, so that the best type of filters are (HE and ADH) because the PUJ and pelvis is much more clear and easy to define after using these kinds of filters. Index Terms: ADH, CLAHE, HE, Image Enhancement, Image Processing, Pelviureteric Junction, Wiener