Skin cancer is one of the most common types of cancer, and early identification is key to better patient outcomes. An optimized YOLOv8, method of detecting skin cancer utilizing the CLEO (Chimpanzee Leader Election Optimization) optimizer to enhance performance is presented in this paper. YOLOv8 incorporates the CLEO optimizer to fine-tune the model's hyperparameters, enhancing the model's efficacy and precision to identify skin lesions. By leveraging YOLOv8's advanced object detection capabilities and CLEO's optimization strengths, this approach offers a highly effective solution in real-time, for automated skin cancer screening, potentially aiding dermatologists in early diagnosis and treatment planning. Extensive experimentation on publicly available ISIC (International Skin Imaging Collaboration), skin cancer dataset demonstrates that the proposed model improves the precision, accuracy, recall, and mAP (mean Average Precision) rates by 2.1%, 2.1%, 6.5%, and 1.1% as compared with the unoptimized algorithm. Further, YOLOv8 integrated with CLEO optimizer attains superior detection accuracy and quicker inference times in comparison to the other cutting-edge methods for skin cancer detection. The analysis can set the stage for practical use.
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