A hierarchical optimisation framework for pigmented lesion diagnosis

CAAI Transactions on Intelligence Technology

article

Audrey Huong¹  KimGaik Tay¹  KokBeng Gan²  Xavier Ngu³ 

[1] Department of Electronic Engineering, Universiti Tun Hussein Onn Malaysia;Department of Electrical, Electronic & Systems Engineering, Universiti Kebangsaan Malaysia;Institute of Integrated Engineering, Universiti Tun Hussein Onn Malaysia

关键词: hierarchical;    hyperparameter;    optimisation;    pigmented lesion;    search;   

DOI  :  10.1049/cit2.12073

学科分类：数学（综合）

来源: Wiley

【 摘 要 】

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.

【 授权许可】

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