An Automated Framework for Patient Identification and Verification Using Deep Learning

Abstract

Automated patient identification and verification are very important at a medical emergency and when patients are not carrying his/her identity. It is a risk factor that identifying the correct patient identity for doctors to provide medical treatment. The majority of the identification or verification is being done by wristbands, RFID tags, fingerprint, face detection by using handcraft feature-based face recognition systems. A new framework based on robust deep learning model and contrast enhancement is proposed in this paper. In the proposed work, the light illumination problem has been addressed by the contrast enhancement technique for deep learning models to recognize the face. It is proved that the inclusion of contrast enhancement is improving patient identification and verification. To evaluate the deep learning framework, the proposed deep learning models have been trained on our own dataset and have been tested with a real-time medical providing agency. The experimental results show that the proposed framework exhibits more robust test results with accuracy than existing hand-crafted techniques under the live webcam video capture for the real-time patient detection system.

An Efficient and Scalable Heart Disease Diagnosis System with Attribute Impact Based Weights and Genetic Correlation Analysis

“…Today artificial neural networks are widely using in design of different real-time applications like medical diagnosis, speech recognition, text processing, image processing etc. In ANN, supervised learning techniques have been employed with the help of non-linear mathematical models [2] to design the artificial neurons, to behave like the human brain in feature selection, data classification, decision making and forecasting. In recent many studies have been focused on the area of heart disease diagnosis system (HDDS) development using popular machine learning techniques like kNN, fuzzy rules, support vector machine, artificial neural networks and other clustering methods.…”