Journal of Artificial Intelligence and Metaheuristics JAIM 2833-5597 10.54216/JAIM https://www.americaspg.com/journals/show/1942 2022 2022 School of Computer Science, University of Petroleum and Energy Studies, Dehradun, 248001, India Sunil Kumar Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt Abdelaziz A. Abdelhamid Computer Science Department, Faculty of Computers and Information, Mansoura University Zahraa Tarek Mathematical programming can express competency concepts in a well-defined mathematical model for a particular. Convolutional Neural Networks (CNNs) and other deep learning models have shown exceptional performance in image categorization tasks. However, questions about their interpretability and reliability are raised by their intrinsic complexity and black-box nature. In this study, we explore the visualization method of Gradient-Weighted Class Activation Mapping (GRAD-CAM) and its application to understanding how CNNs make decisions. We start by explaining why tools like GRAD-CAM are necessary for deep learning and why interpretability is so important. In this article, we provide a high-level introduction to CNN architecture, focusing on the significance of convolutional layers, pooling layers, and fully connected layers in the context of image categorization. Using the Xception model as an illustration, we describe how to generate GRAD-CAM heatmaps to highlight key areas in a picture. We highlight the benefits of GRAD-CAM in terms of localization accuracy and interpretability by comparing it to other visualization techniques like Class Activation Mapping (CAM) and Guided Backpropagation. We also investigate GRAD-CAM's potential uses in other areas of image classification, such as medical imaging, object recognition, and fine-grained classification. We also highlight the disadvantages of GRAD-CAM, such as its vulnerability to adversarial examples and occlusions, along with its advantages. We conclude by highlighting extensions and changes planned to address these shortcomings and strengthen the credibility of GRAD-CAM justifications. As a result of the work presented in this research, we can now analyze and improve Convolutional Image Classifiers with greater accuracy and transparency. 2023 2023 34 42 10.54216/JAIM.040104 https://www.americaspg.com/articleinfo/28/show/1942