Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/3536 2018 2018 Department of Computer Engineering Techniques, College of Technical Engineering, University of Al Maarif, Al Anbar, 31001, Iraq admin admin Citizens Affairs, University Headquarter, University of Anbar, 31001, Ramadi, Anbar, Iraq Noor M Jassam Middle Technical University, Baghdad, Iraq Ahmad Salim Department of Computer Engineering Techniques, College of Technical Engineering, University of Al Maarif, Al Anbar, 31001, Iraq Hussein Ali Bardan Department of Computer Engineering Techniques, College of Technical Engineering, University of Al Maarif, Al Anbar, 31001, Iraq Ahmed Farhan Mutlak Department of Computer Sciences, Sciences, University of Al Maarif, Al Anbar, 31001, Iraq Anas D. Sallibi Department of Computer Engineering Techniques, College of Technical Engineering, University of Al Maarif, Al Anbar, 31001, Iraq Abdalrahman Fatikhan Ataalla Melanoma is one of the most aggressive types of skin cancer, and its early detection is critical to improving survival rates and treatment outcomes for patients. Conventional diagnostic methods often suffer from high computational costs and low accuracy, primarily due to inadequate feature selection and classification strategies. The goal of this research is to combine state-of-the-art deep learning techniques with optimization algorithms to develop a precise and efficient predictive system for melanoma detection. In this work, we propose a novel framework that integrates Convolutional Neural Networks (CNNs) for image classification and a binary Grey Wolf Optimization (GWO) algorithm for feature selection. The binary GWO algorithm identifies the most relevant features from dermatological images, eliminating redundancy and reducing the computational burden. The CNN is then trained on the refined feature subset to enhance classification efficiency. Extensive experiments on publicly available skin lesion datasets demonstrate that the proposed model significantly outperforms traditional machine learning models. Improvements in sensitivity, specificity, and overall classification accuracy highlight the effectiveness of combining deep learning with optimization techniques. Our results show that deep learning and optimization methods, such as the binary GWO algorithm, can be successfully applied to melanoma diagnosis. This strategy not only improves detection efficiency and accuracy but also supports early diagnosis and treatment planning, leading to better patient outcomes. By leveraging the binary GWO algorithm to optimize the feature selection process and CNNs for image classification, the proposed approach reduces computational costs while increasing classification accuracy. When trained and evaluated on publicly available skin lesion datasets, the model demonstrates significant improvements in sensitivity, specificity, and overall accuracy compared to conventional machine learning models. 2025 2025 146 156 10.54216/FPA.180211 https://www.americaspg.com/articleinfo/3/show/3536