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Fusion Based Tongue Color Image Analysis Model for Biomedical Applications

Esam A. AlQaralleh1, Halah Nassif2, Bassam A. Y. Alqaralleh2,*

1 School of Engineering, Princess Sumaya University for Technology, Amman, 11941, Jordan
2 Management Information Systems Department, College of Business Administration, University of Business and Technology, Jeddah, 21448, Saudi Arabia

* Corresponding Author: Bassam A. Y. Alqaralleh. Email: email

Computers, Materials & Continua 2022, 71(3), 5477-5490.


Tongue diagnosis is a novel and non-invasive approach commonly employed to carry out the supplementary diagnosis over the globe. Recently, several deep learning (DL) based tongue color image analysis models have existed in the literature for the effective detection of diseases. This paper presents a fusion of handcrafted with deep features based tongue color image analysis (FHDF-TCIA) technique to biomedical applications. The proposed FDHF-TCIA technique aims to investigate the tongue images using fusion model, and thereby determines the existence of disease. Primarily, the FHDF-TCIA technique comprises Gaussian filtering based preprocessing to eradicate the noise. The proposed FHDF-TCIA model encompasses a fusion of handcrafted local binary patterns (LBP) with MobileNet based deep features for the generation of optimal feature vectors. In addition, the political optimizer based quantum neural network (PO-QNN) based classification technique has been utilized for determining the proper class labels for it. A detailed simulation outcomes analysis of the FHDF-TCIA technique reported the higher accuracy of 0.992.


Cite This Article

E. A. AlQaralleh, H. Nassif and B. A. Y. Alqaralleh, "Fusion based tongue color image analysis model for biomedical applications," Computers, Materials & Continua, vol. 71, no.3, pp. 5477–5490, 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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