Skin Lesion Classification Via Combining Deep Learning Features and Clinical Criteria Representations

Abstract

Skin lesion is a severe disease globally. Early detection of melanoma in dermoscopy images significantly increases the survival rate. However, the accurate recognition of skin lesion is extremely challenging manually visualization. Hence, reliable automatic classification of skin lesions is meaningful to improve pathologists’ accuracy and efficiency. In this paper, we proposed a two-stage method to combine deep learning features and clinical criteria representations to address skin lesion automated diagnosis task. Stage 1 - feature encoding: Modified deep convolutional neural networks (CNNs, in this paper, we used Dense201 and Res50) were fine-tuned to extract rich image global features. To avoid hair noisy, we developed a lesion segmentation U-Net to mask out the decisive regions and used the masked image as CNNs inputs. In addition, color features, texture features and morphological features were exacted based on clinical criteria; Stage 2 - features fusion: LightGBM was used to select the salient features and model parameters, predicting diagnosis confidence for each category. The proposed deep learning frameworks were evaluated on the ISIC 2018 dataset. Experimental results show the promising accuracies of our frameworks were achieved.