Multiple skin lesions diagnostics via integrated deep convolutional networks for segmentation and classification

Mohammed A Al-Masni; Dong-Hyun Kim; Tae-Seong Kim

doi:10.1016/j.cmpb.2020.105351

Multiple skin lesions diagnostics via integrated deep convolutional networks for segmentation and classification

Comput Methods Programs Biomed. 2020 Jul:190:105351. doi: 10.1016/j.cmpb.2020.105351. Epub 2020 Jan 23.

Authors

Mohammed A Al-Masni¹, Dong-Hyun Kim¹, Tae-Seong Kim²

Affiliations

¹ Department of Electrical and Electronic Engineering, College of Engineering, Yonsei University, Seoul, Republic of Korea.
² Department of Biomedical Engineering, College of Electronics and Information, Kyung Hee University, Yongin, Republic of Korea. Electronic address: tskim@khu.ac.kr.

PMID: 32028084
DOI: 10.1016/j.cmpb.2020.105351

Abstract

Background and objective: Computer automated diagnosis of various skin lesions through medical dermoscopy images remains a challenging task.

Methods: In this work, we propose an integrated diagnostic framework that combines a skin lesion boundary segmentation stage and a multiple skin lesions classification stage. Firstly, we segment the skin lesion boundaries from the entire dermoscopy images using deep learning full resolution convolutional network (FrCN). Then, a convolutional neural network classifier (i.e., Inception-v3, ResNet-50, Inception-ResNet-v2, and DenseNet-201) is applied on the segmented skin lesions for classification. The former stage is a critical prerequisite step for skin lesion diagnosis since it extracts prominent features of various types of skin lesions. A promising classifier is selected by testing well-established classification convolutional neural networks. The proposed integrated deep learning model has been evaluated using three independent datasets (i.e., International Skin Imaging Collaboration (ISIC) 2016, 2017, and 2018, which contain two, three, and seven types of skin lesions, respectively) with proper balancing, segmentation, and augmentation.

Results: In the integrated diagnostic system, segmented lesions improve the classification performance of Inception-ResNet-v2 by 2.72% and 4.71% in terms of the F1-score for benign and malignant cases of the ISIC 2016 test dataset, respectively. The classifiers of Inception-v3, ResNet-50, Inception-ResNet-v2, and DenseNet-201 exhibit their capability with overall weighted prediction accuracies of 77.04%, 79.95%, 81.79%, and 81.27% for two classes of ISIC 2016, 81.29%, 81.57%, 81.34%, and 73.44% for three classes of ISIC 2017, and 88.05%, 89.28%, 87.74%, and 88.70% for seven classes of ISIC 2018, respectively, demonstrating the superior performance of ResNet-50.

Conclusions: The proposed integrated diagnostic networks could be used to support and aid dermatologists for further improvement in skin cancer diagnosis.

Keywords: CAD; CNN; Classification; Deep learning; ISIC; Melanoma; Segmentation; Skin lesion.

MeSH terms

Dermoscopy
Diagnosis, Computer-Assisted / methods*
Humans
Image Interpretation, Computer-Assisted / methods*
Machine Learning
Neural Networks, Computer
Skin Neoplasms / classification
Skin Neoplasms / diagnosis*