Journal of Artificial Intelligence and Metaheuristics JAIM 2833-5597 10.54216/JAIM https://www.americaspg.com/journals/show/1941 2022 2022 Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur 56000, Malaysia Wei Hong Lim Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 11152, Egypt Marwa M. Eid Deep Convolutional Networks (CNNs) have revolutionized various fields, including computer vision, but their decision-making process remains largely opaque. To address this interpretability challenge, numerous visual explanation methods have been proposed. However, a comprehensive evaluation and benchmarking of these methods are essential to understand their strengths, limitations, and comparative performance. In this paper, we present a systematic study that benchmarks and compares various visual explanation techniques for deep CNNs. We propose a standardized evaluation framework consisting of benchmark explain ability methods. Through extensive experiments, we analyze the effectiveness, and interpretability of popular visual explanation methods, including gradient-based methods, activation maximization, and attention mechanisms. Our results reveal nuanced differences between the methods, highlighting their trade-offs and potential applications. We conduce a comprehensive evaluation of visual explanation methods on different deep CNNs, the results demonstrate the ability to achieve informed selection and adoption of appropriate techniques for interpretability in real-world applications. 2023 2023 24 33 10.54216/JAIM.040103 https://www.americaspg.com/articleinfo/28/show/1941