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UBC Theses and Dissertations

Automatic translucency detection of basal cell carcinoma (BCC) via deep learning methods Huang, He


Translucency, defined as a jelly-like appearance, is a common clinical feature of basal cell carcinoma (BCC), the most common skin cancer. This feature plays an important role in diagnosing basal cell carcinoma at an early stage because the translucency can be observed readily in clinical examinations with a high specificity. Therefore, translucency detection is a critical component of computer aided systems which aim at early detection of basal cell carcinoma. In this thesis, we proposed two deep learning methods to automatically detect translucency. First, we develop a convolutional neural network based framework to detect translucency of basal cell carcinoma. Furthermore, a sparse auto-encoder based framework is proposed for translucency detection on BCC images. Since currently two types of skin images are mainly used for diagnosis of basal cell carcinoma by doctors, which are dermoscopy images and clinical images, we evaluate two proposed methods on both types of skin images. Our results showed that the two proposed methods yield similar detection performances. For detecting translucency in dermoscopy images, both proposed methods achieve comparable accuracy results, though the accuracy is not as good as we expected. For detecting translucency in clinical images, both methods achieve good performances. Compared the performances in both types of images, the proposed deep learning based methods seems more suitable for translucency detection in clinical images than in dermoscopy images.

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