Fully Connected Visual Words for the Classification of Skin Cancer Confocal Images

Reflectance Confocal Microscopy (RCM) is an ancillary, non-invasive method for reviewing horizontal sections from areas of interest of the skin at a high resolution. In this paper, we propose a method based on the exploitation of Bag of Visual Words (BOVW) technique, coupled with a plain neural network to classify extracted information into discrete patterns of skin cancer types. The paper discusses the technical details of implementation, while providing promising initial results that reach 90% accuracy. Automated classification of RCM images can lead to the establishment of a reliable procedure for the assessment of skin cancer cases and the training of medical personnel through the quantization of image content. Moreover, early detected benign tumours can reduce significantly the number of time and resource consuming biopsies