Heart disease caused of death worldwide, efforts are continued to treat this hard disease, one of the treatments could powerfully infected for a large number of patients is stent placement. However, accurately determining the necessity for a stent based on coronary angiogram data is challenging due to the complexity of the decision-making process. There are a large number of metaheuristic and optimization algorithms so far could serve of these kinds of determining. However, the results of necessity of stent using optimization algorithms are not promising a happiness results. The current study attempted to discover a promising result for necessity of a stent for infected patients of heart disease. An image dataset has been used including stent and notstent images. This study also aiming to improve accuracy in assessing stent necessity for heart disease infects by leveraging ResNet-50 for feature extraction, combining with six optimization algorithms utilized to obtain and present the promising results using five comparative metrics like accuracy, sensitivity, specificity, precision and confusion matrix. Among those algorithms LPBSA overwhelming the other proposed algorithms by scoring 100% for all the five metrics, however PSO competed comprehensively for the majority of the results with the score of 100% for all the metrics excepting sensitivity metric obtaining 75%.