An internet of things based computer vision system for early diagnosis of oral cancer and classification of fabric defect employing deep learning algorithm

Abstract

In advanced healthcare system automated physiological signal newlinemonitoring to elderly sick patient is not only for fast access of data but also to get newlinereliable service by accurate prediction by healthcare service provider. In order to newlineaddress this challenge, the research focus on design of novel Internet of Things newline(IoT) based physiological signal monitoring system to advance e-healthcare newlinesystem. For the realization of proposed system, an advanced deep neural Network newlinebased accurate signal prediction and estimation algorithm is used. The proposed newlinesystem is consisting of an advanced electronics component such as intelligent newlinesensor for signal measurement, National Instrument myRIO for smart data newlineacquisition. Smart-Monitor is designed with intelligent sensor as consumer newlineproduct. To validate the proposed Smart-Monitor system comparison with newlinestandard signal and obtained average accuracy of 97.2% and it shows that the newlineproposed automated system is reliable and accurate monitoring is possible. From newlinethe experimental result it is observed that the proposed system can provide reliable newlineassistance and accurate signal prediction. Wireless physiological signal monitoring system designing with secured data communication in the health care system is an important and dynamic process. Based on the server-side validation of the signal, the data connected to the local server are updated in the cloud. The Internet of thing architecture is used to get the mobility and fast access of patient data to healthcare service providers. In this thesis, a user interface for patient and healthcare service providers for access of physiological signal in a web page and in mobile application is designed and tested experimentally. newline newline