Open Access

ARTICLE

Hemodynamic Analysis and Diagnosis Based on Multi-Deep Learning Models

Xing Deng^1,2, Feipeng Da^1,*, Haijian Shao^2,3

1 School of Automation, Key Laboratory of Measurement and Control for CSE, Ministry of Education, Southeast University, Nanjing, 210096, China
2 School of Computer, Jiangsu University of Science and Technology, Zhenjiang, 212003, China
3 Department of Electrical and Computer Engineering, University of Nevada, Las Vegas, 89154, USA

* Corresponding Author: Feipeng Da. Email:

Fluid Dynamics & Materials Processing 2023, 19(6), 1369-1383. https://doi.org/10.32604/fdmp.2023.024836

Received 08 June 2022; Accepted 25 October 2022; Issue published 30 January 2023

Abstract

This study employs nine distinct deep learning models to categorize 12,444 blood cell images and automatically extract from them relevant information with an accuracy that is beyond that achievable with traditional techniques. The work is intended to improve current methods for the assessment of human health through measurement of the distribution of four types of blood cells, namely, eosinophils, neutrophils, monocytes, and lymphocytes, known for their relationship with human body damage, inflammatory regions, and organ illnesses, in particular, and with the health of the immune system and other hazards, such as cardiovascular disease or infections, more in general. The results of the experiments show that the deep learning models can automatically extract features from the blood cell images and properly classify them with an accuracy of 98%, 97%, and 89%, respectively, with regard to the training, verification, and testing of the corresponding datasets.

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