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

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 inspec-tion 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 assess-ments.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%.