Ultrasonographic evaluation of abnormal uterine bleeding in postmenopausal women

Background: Objectives of current study were to diagnose causes of Abnormal Uterine Bleeding (AUB) in postmenopausal women (PMW) and to correlate it with curettage and histopathological findings, hysteroscopy and thereby minimizing unnecessary interventions in the form of operations and hysteroscopy where sonography depicts normal findings.Methods: After obtaining ethical clearance present prospective observational study was conducted from November 2010 to November 2012, to evaluate the endometrium in 50 postmenopausal women (PMW) with bleeding per vagina referred to the department of Radio diagnosis by the department of gynaecology in Bangalore medical college and research institute. After applying inclusion and exclusion criterias the cases were evaluated with ultrasonography both transabdominal (TAS) and transvaginal scan (TVS where ever necessary). Histopathological and hysteroscopic correlation was done in all cases.Results: 58% of the PMW with bleed were in the age group of 51-60 years. Most common cause of PMB was atrophic endometrium (44%), endometrial polyp (22%), followed by malignancy (14%), and hyperplastic endometrium (6%). At Endometrium thickness less than 4 mm there were nil chances of carcinoma.Conclusions: In women with AUB in postmenopausal age ultrasonography (USG) can be considered as an initial imaging modality for diagnosing endometrial diseases. The sensitivity and specificity of USG for Atrophic endometrium is 100% & 84% respectively with accuracy of 100%, endometrial polyp the specificity is 100% with accuracy of 88%. For malignancy USG showed 100% specificity & accuracy of 100%. Hence USG is highly accurate for evaluating endometrial pathologies. Being noninvasive, less costly & good patient compliance USG should be considered as an initial imaging modality over invasive investigations like D&C, hysteroscopy in evaluating endometrial disorders

Helmet Using GSM and GPS Technology for Accident Detection and Reporting System

An accident is an unexpected and unintended event. In today’s world road accidents stand among the leading cause of human death, Road safety for driver is an essential requirement of society, As the Number of vehicles increase day by day, Collision of vehicle also Increases simultaneously, in this situation this project fulfils the Purpose of saving lives. Helmet is best safety equipment for driver. In this system initially we try to avoid accidents by using, a danger Zone indicator (RF transmitter) circuit, even though the accident occurred the vibration or MEMS sensor will activate the GPS to find the location and further SMS will send to ambulance and family members. This will optimize accidents as well as human death ratio by accidents due to providing proper care with in time frame. Another feature of the proposed system is the ability to detect an accident and send the corresponding geographical coordinates of the accident spot to predefined numbers using a GPS and GSM system respectively. After giving an overview of the system, the paper describes the system architecture, specific components used, logic flow employed and benefits of the system. This proposed system aims at making safety the norm and not a choice

Face Recognition Using Deep Learning

Today face recognition and its usage are  developing at a remarkable rate. Researches are at present building up different strategies in which facial recognition framework works. In circumstances like accidents, normal disasters, missing cases, clashes between nations, kidnappings and numerous different circumstances individuals are regularly isolated by their families. Recognizing the relatives of those refugees is essential to arrive at their family for refugee’s security and backing. Everyday polices are enrolling with missing cases, a portion of those enlisted cases are getting tackled and some are definitely not by using the manual method where it takes more time. The goal of this paper is to provide a solution to overcome time delay from existing strategies for police examination utilizing most recent innovation. Hence we adopt a framework which utilizes CNN (Convolutional Neural Network) technique with VGG16 architecture where we use our raw dataset which contains 84 images collected from 21 families data, after applying augmentation method the image count in final dataset is increased to 1512, then from this dataset 80% of data is used for training data and 20% is used for testing data. This framework helps to verify an individual’s trait using their face and family subtleties with related model with increased accuracy, and gives a effective solution for identifying refugee’s family