Open Access

ARTICLE

Political Optimizer with Deep Learning-Enabled Tongue Color Image Analysis Model

by Anwer Mustafa Hilal^1,*, Eatedal Alabdulkreem², Jaber S. Alzahrani³, Majdy M. Eltahir⁴, Mohamed I. Eldesouki⁵, Ishfaq Yaseen¹, Abdelwahed Motwakel¹, Radwa Marzouk⁶

1 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia
2 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
3 Department of Industrial Engineering, College of Engineering at Alqunfudah, Umm Al-Qura University, Mecca, 24382, Saudi Arabia
4 Department of Information Systems, College of Science & Art at Mahayil, King Khalid University, Abha, 62529, Saudi Arabia
5 Department of Information System, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia
6 Department of Mathematics, Faculty of Science, Cairo University, Giza, 12613, Egypt

* Corresponding Author: Anwer Mustafa Hilal. Email:

Computer Systems Science and Engineering 2023, 45(2), 1129-1143. https://doi.org/10.32604/csse.2023.030080

Received 17 March 2022; Accepted 26 April 2022; Issue published 03 November 2022

Abstract

Biomedical image processing is widely utilized for disease detection and classification of biomedical images. Tongue color image analysis is an effective and non-invasive tool for carrying out secondary detection at anytime and anywhere. For removing the qualitative aspect, tongue images are quantitatively inspected, proposing a novel disease classification model in an automated way is preferable. This article introduces a novel political optimizer with deep learning enabled tongue color image analysis (PODL-TCIA) technique. The presented PODL-TCIA model purposes to detect the occurrence of the disease by examining the color of the tongue. To attain this, the PODL-TCIA model initially performs image pre-processing to enhance medical image quality. Followed by, Inception with ResNet-v2 model is employed for feature extraction. Besides, political optimizer (PO) with twin support vector machine (TSVM) model is exploited for image classification process, shows the novelty of the work. The design of PO algorithm assists in the optimal parameter selection of the TSVM model. For ensuring the enhanced outcomes of the PODL-TCIA model, a wide-ranging experimental analysis was applied and the outcomes reported the betterment of the PODL-TCIA model over the recent approaches.

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Keywords

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