Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/3568 2018 2018 Research Scholar, SoET, CMR University, Bangalore, India; Sr. Assistant Professor, Department of ECE, New Horizon College of Engineering, Bangalore, India Hamsalekha Hamsalekha Professor, ECE and DORI, CMR University, Bangalore, India Glan Devadhas George Associate Professor, School of CSE, Reva University, Bangalore, India T. Y. Satheesha Melanoma is one of the forms of skin cancer that affects people worldwide. Research indicates that nearly 75% of the global population has been impacted by melanoma. Early detection and treatment of melanoma significantly increase survival rates. However, detecting melanoma in its early stages can be challenging because dermatologists typically rely on visual examination and biopsy analysis, which is both time-consuming and labor-intensive. This highlights the need for automated, efficient methods to identify melanoma at earlier stages. Skin cancer is generally classified into two categories: melanoma and benign tumors. The goal of this study is to facilitate the early detection of melanoma by employing deep learning techniques, specifically convolutional neural networks (CNNs), to distinguish between melanoma and benign lesions using the ISIC dataset. The proposed model achieves an accuracy of 80.80%, outperforming previous approaches by offering faster and more accurate melanoma detection. 2025 2025 276 283 10.54216/FPA.180220 https://www.americaspg.com/articleinfo/3/show/3568