Unleashing the power of Manta Rays Foraging Optimizer: A novel approach for hyper-parameter optimization in skin cancer classification

• This work proposes a novel hyperparameter optimization strategy using Manta Rays Foraging Optimizer (MRFO) for a Convolutional Neural Network (CNN) to improve skin cancer classification accuracy. This work proposes a novel hyperparameter optimization strategy using MRFO for CNN in skin cancer classification. • The MRFO-based model achieves state-of-the-art performance on multiple datasets (ISIC: 99.43 % accuracy, 0.0250 loss; PH2: 99.96 % accuracy, 0.0033 loss; HAM10000: 97.70 % accuracy, 0.0626 loss). The MRFO model achieves 99.43% accuracy (0.0250 loss) on ISIC, 99.96% accuracy (0.0033 loss) on PH2, and 97.70% on HAM10000. • It outperforms conventional optimizers like Grey Wolf Optimizer (98.33 % accuracy, 0.17 loss) demonstrating significant improvement in both accuracy and loss reduction. It outperforms traditional optimizers like Grey Wolf Optimizer, achieving 98.33% accuracy and 0.17 loss. Optimizing hyperparameters is crucial for improving the performance of deep learning (DL) models, especially in complex applications like skin cancer classification from dermoscopic images. This study introduces a novel hyperparameter optimization strategy using the Manta Rays Foraging Optimizer (MRFO). A model tailored for skin cancer classification is created by fine-tuning a Convolutional Neural Network (CNN) with MRFO, coupled with in-depth image preprocessing. Empirical evaluations on diverse datasets (ISIC, PH2, HAM10000) showcase the significant superiority of the MRFO-based model over conventional optimization algorithms. The model achieves impressive accuracy and loss metrics (ISIC: 99.43 %, 0.0250; PH2: 99.96 %, 0.0033; HAM10000: 97.70 %, 0.0626), outperforming alternative optimization algorithms such as the Grey Wolf Optimizer (98.33 % accuracy, 0.17 loss), Whale Optimization Algorithm (96 % accuracy), Grasshopper Optimization Algorithm (97.2 % accuracy), Densnet121-MRFO (99.26 % accuracy), InceptionV3 with GA (99.9 % accuracy), and African Vulture Optimization Algorithm (92.7 % accuracy). The novel approach demonstrates superior accuracy and loss metrics, underscoring its potential for precise and efficient skin cancer detection. Additionally, narrow confidence intervals and balanced precision-recall confirm the model’s generalizability and effectiveness, paving the way for early and accurate skin cancer detection and potentially improving patient outcomes.