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Simulated Annealing with Deep Learning Based Tongue Image Analysis for Heart Disease Diagnosis

S. Sivasubramaniam*, S. P. Balamurugan

Department of Computer and Information Science, Annamalai University, Chidambaram, 608002, India

* Corresponding Author: S. Sivasubramaniam. Email:

Intelligent Automation & Soft Computing 2023, 37(1), 111-126.


Tongue image analysis is an efficient and non-invasive technique to determine the internal organ condition of a patient in oriental medicine, for example, traditional Chinese medicine (TCM), Japanese traditional herbal medicine, and traditional Korean medicine (TKM). The diagnosis procedure is mainly based on the expert's knowledge depending upon the visual inspection comprising color, substance, coating, form, and motion of the tongue. But conventional tongue diagnosis has limitations since the procedure is inconsistent and subjective. Therefore, computer-aided tongue analyses have a greater potential to present objective and more consistent health assessments. This manuscript introduces a novel Simulated Annealing with Transfer Learning based Tongue Image Analysis for Disease Diagnosis (SADTL-TIADD) model. The presented SADTL-TIADD model initially pre-processes the tongue image to improve the quality. Next, the presented SADTL-TIADD technique employed an EfficientNet-based feature extractor to generate useful feature vectors. In turn, the SA with the ELM model enhances classification efficiency for disease detection and classification. The design of SA-based parameter tuning for heart disease diagnosis shows the novelty of the work. A wide-ranging set of simulations was performed to ensure the improved performance of the SADTL-TIADD algorithm. The experimental outcomes highlighted the superior of the presented SADTL-TIADD system over the compared methods with maximum accuracy of 99.30%.


Cite This Article

S. Sivasubramaniam and S. P. Balamurugan, "Simulated annealing with deep learning based tongue image analysis for heart disease diagnosis," Intelligent Automation & Soft Computing, vol. 37, no.1, pp. 111–126, 2023.

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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