Siamese Neural Networks for Skin Cancer Classification and New Class Detection using Clinical and Dermoscopic Image Datasets

TL;DR

This paper explores Siamese Neural Networks for skin cancer detection, enabling classification and out-of-class lesion identification using clinical and dermoscopic images, which enhances clinical applicability over traditional classifiers.

Contribution

It introduces the use of Siamese Neural Networks for skin lesion classification and out-of-class detection, addressing limitations of existing models in clinical settings.

Findings

Achieved 74.33% accuracy on clinical images

Achieved 85.61% accuracy on dermoscopic images

SNNs can detect out-of-class skin lesions

Abstract

Skin cancer is the most common malignancy in the world. Automated skin cancer detection would significantly improve early detection rates and prevent deaths. To help with this aim, a number of datasets have been released which can be used to train Deep Learning systems - these have produced impressive results for classification. However, this only works for the classes they are trained on whilst they are incapable of identifying skin lesions from previously unseen classes, making them unconducive for clinical use. We could look to massively increase the datasets by including all possible skin lesions, though this would always leave out some classes. Instead, we evaluate Siamese Neural Networks (SNNs), which not only allows us to classify images of skin lesions, but also allow us to identify those images which are different from the trained classes - allowing us to determine that an image is not an example of our training classes. We evaluate SNNs on both dermoscopic and clinical images of skin lesions. We obtain top-1 classification accuracy levels of 74.33% and 85.61% on clinical and dermoscopic datasets, respectively. Although this is slightly lower than the state-of-the-art results, the SNN approach has the advantage that it can detect out-of-class examples. Our results highlight the potential of an SNN approach as well as pathways towards future clinical deployment.