Research Article An Efficient Multilevel Thresholding Scheme for Heart Image Segmentation Using a Hybrid Generalized Adversarial Network A. Mallikarjuna Reddy , 1 K. S. Reddy , 2 M. Jayaram , 3 N. Venkata Maha Lakshmi , 4 Rajanikanth Aluvalu , 5 T. R. Mahesh , 6 V. Vinoth Kumar , 6 and D. Stalin Alex 7 1 Department of Computer Science & Engineering, Anurag University, Hyderabad, India 2 Department of Information Technology, Anurag University, Hyderabad, India 3 Department of CSE (Data Science), Sreyas Institute of Engineering & Technology, Hyderabad, India 4 Department of CSE, PSCMR College of Engineering & Technology, Vijayawada, India 5 Department of Information Technology, Chaitanya Bharathi Institute of Technology, Hyderabad, India 6 Department of Computer Science and Engineering, Jain (Deemed-to-be University), Bengaluru, India 7 Department of Computer Science and Engineering (Data Science), State University of Bangladesh, Dhaka, Bangladesh Correspondence should be addressed to D. Stalin Alex; drstalinalex.cse@sub.edu.bd Received 24 August 2022; Revised 21 September 2022; Accepted 30 September 2022; Published 8 November 2022 Academic Editor: Sweta Bhattacharya Copyright © 2022 A. Mallikarjuna Reddy et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Most people worldwide, irrespective of their age, are suffering from massive cardiac arrest. To detect heart attacks early, many researchers worked on the clinical datasets collected from different open-source datasets like PubMed and UCI repository. However, most of these datasets have collected nearly 13 to 147 raw attributes in textual format and implemented traditional data mining approaches. Traditional machine learning approaches just analyze the data extracted from the images, but the extraction mechanism is inefficient and it requires more number of resources. The authors of this research article proposed a system that is aimed at predicting heart attacks by integrating the techniques of computer vision and deep learning approaches on the heart images collected from the clinical labs, which are publicly available in the KAGGLE repository. The authors collected live images of the heart by scanning the images through IoT sensors. The primary focus is to enhance the quality and quantity of the heart images by passing through two popular components of GAN. GAN introduces noise in the images and tries to replicate the real-time scenarios. Subsequently, the available and newly created images are segmented by applying a multilevel threshold operation to find the region of interest. This step helps the system to predict the accurate attack rate by considering various factors. Earlier researchers have obtained sound accuracy by generating similar heart images and found the ROI parts of the 2D echo images. The proposed methodology has achieved an accuracy of 97.33% and a 90.97% true-positive rate. The reason for selecting the computed tomography (CT-SCAN) images is due to the gray scale images giving more reliable information at a low computational cost. 1. Introduction Using the CNN, the entire image is processed which requires lot of resources and needs high-end GPU which makes the deployment of the model expensive. Image segmentation can find the region of interest by clustering the pixels with homogenous labels. Since working with only fewer parts of the images reduces the resources, it is more efficient than the CNN. This process also enhances the granularity of the images by focusing only on the characteristics that are Hindawi Journal of Sensors Volume 2022, Article ID 4093658, 11 pages https://doi.org/10.1155/2022/4093658