Survey of Multilingual Script Identification Techniques on Wild Images

Multilingual Script Identification on natural images has recently increase research attention and this is very challenging task. This paper presents a review of latest techniques for the multilingual scripts. The system can choose the appropriate Optical Character Recognition (OCR) engine to recognize a script based here on script identity of a retrieved line of text or word. A number of approaches for identifying different characters, including such Japanese, Chinese, Arabic, Korean and Indian, have been developed. scripts are used in written on natural scenes captured by a voyager from cameras or text recognitions system. Here we also present the difficulties that come with script identification, methods used for features extraction and also the classifiers used for identification. We provided a comprehensive description and evaluation of previous and state-of-the-art script identification approaches. It should be emphasized that researchers in the area of multilingual script recognition is still in its early stages, and additional analysis is needed

Blunt abdominal trauma patients presenting to a tertiary care facility of Pakistan: a three years experience

Blunt abdominal trauma (BAT) refers to injuries without wounds entering the peritoneal cavity due to road traffic accidents (RTA) and falls, as a result of collision or counter collision. The objective of the present study was to determine the frequency of patients with visceral injuries in blunt abdominal trauma. This study was carried out in the Department of Surgery, including ward-3 of Jinnah Post Graduate Medical Centre, Karachi, from November 2017 till November 2020. The study design was descriptive observational and cross sectional. During the study period, the data of 112 patients was collected, which comprised of 102 males and 10 females. All the patients between 12 to 65 years of age (mean age:31.84 ± 13.14 years) presenting to the emergency with < 24 hours of abdominal trauma, were included in the study. Organs involved during blunt abdominal trauma were observed and frequency was recorded. ---Continu

Synthesis of 2-Aminopyrimidine Derivatives and Their Evaluation as &beta;-Glucuronidase Inhibitors: In Vitro and In Silico Studies

Currently the discovery and development of potent &beta;-glucuronidase inhibitors is an active area of research due to the observation that increased activity of this enzyme is associated with many pathological conditions, such as colon cancer, renal diseases, and infections of the urinary tract. In this study, twenty-seven 2-aminopyrimidine derivatives 1&ndash;27 were synthesized by fusion of 2-amino-4,6-dichloropyrimidine with a variety of amines in the presence of triethylamine without using any solvent and catalyst, in good to excellent yields. All synthesized compounds were characterized by EI-MS, HREI-MS and NMR spectroscopy. Compounds 1&ndash;27 were then evaluated for their &beta;-glucuronidase inhibitory activity, and among them, compound 24 (IC50 = 2.8 &plusmn; 0.10 &micro;M) showed an activity much superior to standard D-saccharic acid 1,4-lactone (IC50 = 45.75 &plusmn; 2.16 &micro;M). To predict the binding mode of the substrate and &beta;-glucuronidase, in silico study was performed. Conclusively, this study has identified a potent &beta;-glucuronidase inhibitor that deserves to be further studied for the development of pharmaceutical products

Segmentation of Liver Tumor in CT Scan Using ResU-Net

Segmentation of images is a common task within medical image analysis and a necessary component of medical image segmentation. The segmentation of the liver and liver tumors is an important but challenging stage in screening and diagnosing liver diseases. Although many automated techniques have been developed for liver and tumor segmentation; however, segmentation of the liver is still challenging due to the fuzzy & complex background of the liver position with other organs. As a result, creating a considerable automated liver and tumour division from CT scans is critical for identifying liver cancer. In this article, deeply dense-network ResU-Net architecture is implemented on CT scan using the 3D-IRCADb01 dataset. An essential feature of ResU-Net is the residual block and U-Net architecture, which extract additional information from the input data compared to the traditional U-Net network. Before being fed to the deep neural network, image pre-processing techniques are applied, including data augmentation, Hounsfield windowing unit, and histogram equalization. The ResU-Net network performance is evaluated using the dice similarity coefficient (DSC) metric. The ResU-Net system with residual connections outperformed state-of-the-art approaches for liver tumour identification, with a DSC value of 0.97% for organ recognition and 0.83% for segmentation methods