Exploring fetal brain tumor glioblastoma symptom verification with self organizing maps and vulnerability data analysis.

Brain tumor glioblastoma is a disease that is caused for a child who has abnormal cells in the brain, which is found using MRI "Magnetic Resonance Imaging" brain image using a powerful magnetic field, radio waves, and a computer to produce detailed images of the body's internal structures it is a standard diagnostic tool for a wide range of medical conditions, from detecting brain and spinal cord injuries to identifying tumors and also in evaluating joint problems. This is treatable, and by enabling the factor for happening, the factor for dissolving the dead tissues. If the brain tumor glioblastoma is untreated, the child will go to death; to avoid this, the child has to treat the brain problem using the scan of MRI images. Using the neural network, brain-related difficulties have to be resolved. It is identified to make the diagnosis of glioblastoma. This research deals with the techniques of max rationalizing and min rationalizing images, and the method of boosted division time attribute extraction has been involved in diagnosing glioblastoma. The process of maximum and min rationalization is used to recognize the Brain tumor glioblastoma in the brain images for treatment efficiency. The image segment is created for image recognition. The method of boosted division time attribute extraction is used in image recognition with the help of MRI for image extraction. The proposed boosted division time attribute extraction method helps to recognize the fetal images and find Brain tumor glioblastoma with feasible accuracy using image rationalization against the brain tumor glioblastoma diagnosis. In addition, 45% of adults are affected by the tumor, 40% of children and 5% are in death situations. To reduce this ratio, in this study, the Brain tumor glioblastoma is identified and segmented to recognize the fetal images and find the Brain tumor glioblastoma diagnosis. Then the tumor grades were analyzed using the efficient method for the imaging MRI with the diagnosis result of partially high. The accuracy of the proposed TAE-PIS system is 98.12% which is higher when compared to other methods like Genetic algorithm, Convolution neural network, fuzzy-based minimum and maximum neural network and kernel-based support vector machine respectively. Experimental results show that the proposed method archives rate of 98.12% accuracy with low response time and compared with the Genetic algorithm (GA), Convolutional Neural Network (CNN), fuzzy-based minimum and maximum neural network (Fuzzy min-max NN), and kernel-based support vector machine. Specifically, the proposed method achieves a substantial improvement of 80.82%, 82.13%, 85.61%, and 87.03% compared to GA, CNN, Fuzzy min-max NN, and kernel-based support vector machine, respectively.

Ultrasound Fetal Image Segmentation Techniques: A Review.

BACKGROUND: This paper reviews segmentation techniques for 2D ultrasound fetal images. Fetal anatomy measurements derived from the segmentation results are used to monitor the growth of the fetus. DISCUSSION: The segmentation of fetal ultrasound images is a difficult task due to inherent artifacts and degradation of image quality with gestational age. There are segmentation techniques for particular biological structures such as head, stomach, and femur. The whole fetal segmentation algorithms are only very few. CONCLUSION: This paper presents a review of these segmentation techniques and the metrics used to evaluate them are summarized.

Plasmid Profile Analysis and bla VIM Gene Detection of Metalo ß-lactamase (MBL) Producing Pseudomonas aeruginosa Isolates from Clinical Samples.

INTRODUCTION: Pseudomonas aeruginosa is a frequent colonizer of hospitalized patients. They are responsible for serious infections such as meningitis, urological infections, septicemia and pneumonia. Carbapenem resistance of Pseudomonas aeruginosa is currently increasingly reported which is often mediated by production of metallo-ß-lactamase (MBL). Multidrug resistant Pseudomonas aeruginosa isolates may involve reduced cell wall permeability, production of chromosomal and plasmid mediated ß lactamases, aminoglycosides modifying enzymes and an active multidrug efflux mechanism. OBJECTIVE: This study is aimed to detect the presence and the nature of plasmids among metallo-ß-lactamase producing Pseudomonas aeruginosa isolates. Also to detect the presence of bla VIM gene from these isolates. MATERIALS AND METHODS: Clinical isolates of Pseudomonas aeruginosa showing the metalo-ß-lactamase enzyme (MBL) production were isolated. The MBL production was confirmed by three different methods. From the MBL producing isolates plasmid extraction was done by alkaline lysis method. Plasmid positive isolates were subjected for blaVIM gene detection by PCR method. RESULTS: Two thousand seventy six clinical samples yielded 316 (15.22%) Pseudomonas aeruginosa isolates, out of which 141 (44.62%) were multidrug resistant. Among them 25 (17.73%) were metallo-ß-lactamase enzyme producers. Plasmids were extracted from 18 out of 25 isolates tested. Five out of 18 isolates were positive for the blaVIM gene detection by the PCR amplification. CONCLUSION: The MBL producers were susceptible to polymyxin /colistin with MIC ranging from 0.5 - 2µg/ml. Molecular detection of specific genes bla VIM were positive among the carbapenem resistant isolates.