Metaheuristic algorithm based hyper-parameters optimization for skin lesion classification

The most dangerous type of skin cancer in the world is Melanoma. Early diagnosis of this cancer in primary stages can increase the chance of surviving death. In recent years, automatic skin cancer detection systems have played a significant role in increasing the rate of cancer diagnosis. Although deep convolutional neural networks presented advantages over traditional methods and brought tremendous breakthroughs in many image classification tasks, accurate classification of skin lesions remains challenging due to the complexity of choosing appropriate architecture for deep neural networks and hyper-parameter tuning. The aim of this paper is to increase the performance of skin lesion classification system through optimizing hyper-parameters and architecture of deep neural network using metaheuristic optimization algorithms. For this purpose, three optimization algorithms are employed to find an optimal configuration for the convolutional neural network either in pre-trained models or model that are trained from scratch. Then the deep features extracted from the optimized models were fused together in pairs and used to train a KNN classifier. The effect of applying hyper-parameter optimization is evaluated on ISIC 2017 and ISIC 2018 datasets. The accuracy of the deep neural network produced by our method reaches to 81.6% and F1-score of 80.9% on ISIC 2017 dataset and accuracy of 90.1% and F1-score of 89.8% on ISIC 2018. The results of the present study indicate that the proposed method outperforms similar methods in classifying seven and three classes of images, without requiring heavy preprocessing and segmentation steps.