A Hybrid Model for Classification of Skin Cancer Images After Segmentation

Abstract

For dermatoscopic skin lesion images, deep learning-based algorithms, particularly convolutional neural networks (CNN), have demonstrated good classification and segmentation capabilities. The impact of utilizing lesion segmentation data on classification performance, however, is still up for being subject to discussion. Being driven in this direction, in this work we propose a hybrid deep learning-based model to classify the skin cancer using segmented images. In the first stage, a fully convolutional encoder–decoder network (FCEDN) is employed to segment the skin cancer image and then in the second phase, a CNN is applied on the segmented images for classification. As the model’s success depends on the hyper-parameters it uses and fine-tuning these hyper-parameters by hand is time-consuming, so in this study the hyper-parameters of the hybrid model are optimized by utilizing an exponential neighborhood gray wolf optimization (ENGWO) technique. Extensive experiments are carried out using the International Skin Imaging Collaboration (ISIC) 2016 and ISIC 2017 datasets to show the efficacy of the model. The suggested model has been evaluated on both balanced and unbalanced datasets. With the balanced dataset, the proposed hybrid model achieves training accuracy up to 99.98%, validation accuracy up to 92.13% and testing accuracy up to 89.75%. It is evident from the findings that the proposed hybrid model outperforms previous known models in a competitive manner over balanced data.