Akademik Veri Yönetim Sistemi
Journal of Optoelectronics and Advanced Materials, cilt.24, sa.11-12, ss.558-562, 2022 (SCI-Expanded)
Knowledge of optical properties of blood is very important to solve problems presented by biomedical optics. The development of predictive models of blood is a challenging task due to the inherent complexity of biological systems. In this paper, we investigated the optical parameter responsible for the appearance attributes of whole blood. Blood, an extraordinary fluid, makes life possible. The circulation of blood through the body results to function and fights diseases or infections. This work focuses on generative computational simulations, modelling, and deep learning techniques to gain data-driven insights about the valuable fluid of whole blood and its properties. The analysis of optical properties of blood is essential to interpret its interaction with light to accurately diagnose illnesses. In recent years, the studies of the association between blood and previously known symptoms have attracted increasing attention. The bibliometric analysis showed the trends of whole blood and its parameters in an increasing pattern. In this study, we proposed an interpretable deep learning strategy incorporating the neural network to examine the refractive index of blood data. The new proposed method fully employed raw data and allowed for implementation of a build-up and a validation model. The developed model was used to successfully diagnose, detect and define the diseases in a manner that is noninvasive, simple, accurate and completely cost-effective. These intelligent models can play an important role in future biomedical applications in design and improvements to be made on the performance of optical devices.