Deep learning, sparse coding, and SVM for melanoma recognition in dermoscopy images

Abstract

This work presents an approach for melanoma recognition in dermoscopy images that combines deep learning, sparse coding, and support vector machine (SVM) learning algorithms. One of the beneficial aspects of the proposed approach is that unsupervised learning within the domain, and feature transfer from the domain of natural photographs, eliminates the need of annotated data in the target task to learn good features. The applied feature transfer also allows the system to draw analogies between observations in dermoscopic images and observations in the natural world, mimicking the process clinical experts themselves employ to describe patterns in skin lesions. To evaluate the methodology, performance is measured on a dataset obtained from the International Skin Imaging Collaboration, containing 2624 clinical cases of melanoma (334), atypical nevi (144), and benign lesions (2146). The approach is compared to the prior state-of-art method on this dataset. Two-fold cross-validation is performed 20 times for evaluation (40 total experiments), and two discrimination tasks are examined: 1) melanoma vs. all non-melanoma lesions, and 2) melanoma vs. atypical lesions only. The presented approach achieves an accuracy of 93.1% (94.9% sensitivity, and 92.8% specificity) for the first task, and 73.9% accuracy (73.8% sensitivity, and 74.3% specificity) for the second task. In comparison, prior state-of-art ensemble modeling approaches alone yield 91.2% accuracy (93.0% sensitivity, and 91.0% specificity) first the first task, and 71.5% accuracy (72.7% sensitivity, and 68.9% specificity) for the second. Differences in performance were statistically significant (p < 0.05), suggesting the proposed approach is an effective improvement over prior state-of-art.