Ultrasound-based internal carotid artery plaque characterization using deep learning paradigm on a supercomputer: a cardiovascular disease/stroke risk assessment system

Abstract

Background: Abduction, swapping and mix-ups are the unfortunate events that could happen to newborn while in hospital premises and medical personnel are finding it difficult to curb this unfortunate incident. Accurate patient identification (ID) is essential for patient safety, especial-ly with our smallest and most vulnerable paediatric patients. The level of security is very crucial issue in maternity ward and the problem of missing and swapping of newborn is of prime concern to the persons involved and affected. There is a common perception in the society that nothing can be done to prevent this unfortunate tragedy. In comparison to developed nations the developing countries are facing more challenges because of overcrowding and scarcity of medical facilities in the hospital. The face of a newborn baby changes appearance in different lighting conditions in spite of various contrast enhancement techniques. The class of images which are under similar environment conditions (inter-class) may result in more differences than that of the intra-class images under different illumination conditions. This gives rise to the misclassification of images. Objective: The main objective of this paper is perform newborn face recognition in a hybrid approach. Speeded Up Robust Features (SURF) and Local Binary Pattern (LBP). Methods: The scientific contributions of the proposed work are stated below: • For face recognition of newborns in a semi controlled environment. • Overcoming the pose and illumination challenge. • Uses single gallery image. • Uses a hybrid approach to improve the results. Results: The average Rank 1 accuracy of the proposed method is 93.65% whereas that of the existing algorithms is quite low. The proposed technique is 12.8% more accurate than LBP, 10.5% more accurate than SURF, 12.8% more accurate than LDA and 18.1% more accurate than PCA for rank1. Conclusion: In this paper new semi supervised technique is used for demonstrate the improve the performance of the newborn face recognition system in different illumination and pose conditions. In our. © 2021 Bentham Science Publishers.