Adaptive hybrid hyperparameter optimization with MRFO and Lévy flight for accurate melanoma classification Academic Article uri icon

abstract

  • Abstract Hyperparameter optimization (HPO) is essential for deep learning in medical image classification, yet standard metaheuristics such as Manta Ray Foraging Optimization (MRFO) often suffer from premature convergence in high-dimensional search spaces. To address these limitations, an enhanced variant, MRFO-LF, was proposed by incorporating Lévy flight-based exploration, adaptive step-size decay, and a hybrid stochastic–deterministic search mechanism. This work details the first application of the proposed MRFO-LF to HPO in melanoma classification, a critical task within medical image analysis. The Lévy component enables long-range perturbations, while the adaptive decay mechanism gradually narrows the search scope, and the hybrid strategy balances global versus local exploration without relying on problem-specific heuristics. Experiments were conducted on the ISIC and PH $$ ^2 $$ 2 dermoscopic datasets using DenseNet121, InceptionV3, and VGG19. MRFO-LF attained peak validation accuracies of 99.49% (ISIC) and 100.00% (PH $$ ^2 $$ 2 ) for DenseNet121, with corresponding validation losses of 0.3580 and 0.0015. When compared to MRFO, PSO, and GA, the proposed method improved ISIC accuracy by 0.40%, reduced PH $$ ^2 $$ 2 loss by over 95%, and converged up to 30% faster. Statistical significance was confirmed through ANOVA and paired t-tests ( $$ p < 0.05 $$ p < 0.05 ). These results position MRFO-LF as a reliable and efficient optimizer for complex hyperparameter tuning in medical image classification.

publication date

  • 2025

start page

  • 64

volume

  • 37

issue

  • 4