The study of training hyperparameters optimisation problems remains underexplored in skin lesion research. This is the first report of using hierarchical optimisation to improve computational effort in a four-dimensional search space for the problem. The authors explore training parameters selection in optimising the learning process of a model to differentiate pigmented lesions characteristics. In the authors' demonstration, pretrained GoogleNet is fine-tuned with a full training set by varying hyperparameters, namely epoch, mini-batch value, initial learning rate, and gradient threshold. The iterative search of the optimal global-local solution is by using the derivative-based method. The authors used non-parametric one-way ANOVA to test whether the classification accuracies differed for the variation in the training parameters. The authors identified the mini-batch size and initial learning rate as parameters that significantly influence the model's learning capability. The authors' results showed that a small fraction of combinations (5%) from constrained global search space, in contrarily to 82% at the local level, can converge with early stopping conditions. The mean (standard deviation, SD) validation accuracies increased from 78.4 (4.44)% to 82.9 (1.8)% using the authors' system. The fine-tuned model's performance measures evaluated on a testing dataset showed classification accuracy, precision, sensitivity, and specificity of 85.3%, 75.6%, 64.4%, and 97.2%, respectively. The authors' system achieves an overall better diagnosis performance than four state-of-the-art approaches via an improved search of parameters for a good adaptation of the model to the authors' dataset. The extended experiments also showed its superior performance consistency across different deep networks, where the overall classification accuracy increased by 5% with this technique. This approach reduces the risk of search being trapped in a suboptimal solution, and its use may be expanded to network architecture optimisation for enhanced diagnostic performance. K E Y W O R D S hierarchical, hyperparameter, optimisation, pigmented lesion, search | INTRODUCTIONDermatology is one of the most important fields of medicine that deals with diseases related to skin and cosmetic problems. This disease accounts for one-third of cancers diagnosed worldwide [1, 2] and will continue to be on the rise. Among the common challenges in the practice of dermatology is the exact diagnosis of skin cancer after a thorough physical examination, even at an early stage. Most skin lesions exhibit visually indiscernible characteristics [3], so morphological features that are looked after by dermatologists in their diagnosis include colour, boundary regularity, size, and texture of the examined skin [4, 5] using either ABCD rule, a seven-point checklist, or Menzies method [6,7]. This abnormal discolouration in the outer skin layer can be classified into benign and malignant skin lesions. While benign lesions are generally not invasiveThis is an open access arti...